DeepITE: Designing Variational Graph Autoencoders for Intervention Target Estimation
Authors: Hongyuan Tao
Hang Yu
Jianguo Li
Venue: NeurIPS 2024 poster (2024)
Keywords: Intervention Target Estimation; Intervention Recognition; VGAE; semi-supervised; self-supervised
Primary area: causal_inference
TL;DR: DeepITE is a carefully-designed VGAE that facilitates collaborative and amortized inference for estimating intervention targets across diverse datasets and causal graphs.

Abstract: Intervention Target Estimation (ITE) is vital for both understanding and decision-making in complex systems, yet it remains underexplored. Current ITE methods are hampered by their inability to learn from distinct intervention instances collaboratively and to incorporate rich insights from labeled data, which leads to inefficiencies such as the need for re-estimation of intervention targets with minor data changes or alterations in causal graphs. In this paper, we propose DeepITE, an innovative deep learning framework designed around a variational graph autoencoder. DeepITE can concurrently learn from both unlabeled and labeled data with different intervention targets and causal graphs, harnessing correlated information in a self or semi-supervised manner. The model's inference capabilities allow for the immediate identification of intervention targets on unseen samples and novel causal graphs, circumventing the need for retraining. Our extensive testing confirms that DeepITE not only surpasses 13 baseline methods in the Recall@k metric but also demonstrates expeditious inference times, particularly on large graphs. Moreover, incorporating a modest fraction of labeled data (5-10\%) substantially enhances DeepITE's performance, further solidifying its practical applicability. Our source code is available at https://github.com/alipay/DeepITE.

DeepITE: Designing Variational Graph Autoencoders for Intervention Target Estimation
Hongyuan Tao"
Hang Yu" Ant Group Hangzhou, China hyu www..hgooantggrup.ccom
Jianguo Lit Ant Group Hangzhou, China lijg www.@antgruu.pccoommmm com
Ant Group Hangzhou, China thy qyyantgroup.com com
Abstract
Intervention Target Estimation (TTE) is vital for both understanding and decision- making in complex systems, yet remains underexplored. Current ITT methods are hampered by their inability to learn from distinet intervention instances col- laboratively and to incorporate rich insights from labeled data, which leads to inefficiencies such the need for re-estimation of intervention targets with minor data changes or alterations in causal graphs. In this paper, we propose DeepITE, innovative deep learning framework designed around variational graph autoen- coder. DeepITE can concurrently learn from both unlabeled and labeled data with different intervention targets and causal graphs. harnessing correlated information in a self or supervisde manner. The model's 's inference capabilities allow for the immediate identificatinn of intervention targets on unseen samples and novel causal graphs, circumventing the need for retraining. Our extensive testing con- firms that DeepITT not only surpasses 13 baseline methods in the Recall@k metric but also demonstrates expeditious inference times, particularly on large graphs. Moreover, incorporating a modest fraction of labeled data (5-10%) substantially enhances DeepITE's performance, further solidifying its practical applicability. com ham/aalpay/DeeepT/
Our source code is available at
Introduction
Causal analysis in complex systems encompasses series of steps beginning with causal discovery (.. which aims to delineate the causal structure, followed by the identification and estimation of causal effects [2]. Within this framework, a critical yet often overlooked component is Intervention Target Estimation (ITE) [3]. alternatively known as Intervention Recognition [4]. ITE is the process of pinpointing which variables in a system have been subject to intervention, particularly when such interventions are opaque or not directly manipulable. This process not only fosters deeper under- standing of the causal mechanisms driving specific outcomes, which resonates with the principles of explainable AI (XAI), but also plays a pivotal role in recognizing variables that can be strategically altered produce desired effects, aligning with the concept of algorithmic recourse [5].
To illustrate, consider the application ITE in root cause analysis (RCA) within a microservices system. These systems consist of network of services working in concert deliver software functionality. When a service failure occurs, such as a system outage or performance degradation, becomes imperative identify the root cause. ITE is the key that unlocks definitive insight into the RCA process. It methodically pinpoints the specific services whose cnlfunettion-ssemmiin from network issues, hardware failures, security breaches- the anomalies in question. ITE
"Equal contribution. *Correpooning author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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only equips operators with a clear and logical explanation for the system's 's alerts, enhancing their comprehension of the issues at hand, but it also empowers them to implement immediate and effective countermeasures and fortify the system against future incidents. Beyond RCA in microservice systems, ITE''s applicability extends to a multitude of domains, from unraveling the genetic factors involved in diseases within biomedicine, to tracing the determinants of user behavior for marketing.
Unfortunately, the field of ITE has not been thoroughly explored, often resulting in intervention targets being relegated to secondary outputs from causal discovery rather than being a dedicated field of inquiry. Only recently, Varici er al. [ pioneered the exclusive study of learning intervention targets in linear SCMs. In parallel, Li et al. approached the problem from the perspective of RCA, coining it intervention recognition. Finally. Yang et al. (7] extended ITE to non-linear SCMs. This handful of methods can only identify the targets for an intervention instance with a large sample size, relying on an accompanying dataset of known observational data, all within the confines of a fixed causal graph. The drawbacks of these strategies are twofold: From the learning perspective, they independently map data to intervention targets for each intervention instance, disregarding the potential correlations among distinct instances. In RCA scenarios, for example, various incidents could stem from the same underlying service problem. A collaborative learning approach, which considers all instances collectively, could more effectively elucidate the data-intervention target relationships. Moreover, these methods often neglect the labeled data that are often available, such as those obtained from controlled chaos engineering exercises that identify specific services as failure root causes. Consequently, opportunities to refine and expedite future similar analyses are lost. From the inference standpoint, slight changes in the data in the graph structure necessitate burdensome and complete re-estimation of intervention targets. In RCA contexts, this means that despite shared causality between distinet incidents, occurrence, leading to extended system downtime and delayed resolutions. Additionally, the premise that a large volume of data pertains a uniform set of intervention targets is restrictive. Such data are challenging to obtain, which further complicates the assurance of these methods' performance.
still require piecemeal analyses for each new
Addressing these shortcomings, we introduce DeepITE, an innovative deep-learning solution that disentangles the learning and inference processes. In particular, we design a variational graph autoencoder (VGAE) that can concurrently learn across diverse causal graphs and sets of intervention targets in a self-supervised or semi-supervised mode, thus, effectively harnesses correlated information to unravel the intricate relationship between input data and intervention targets. Once the VGAE is trained, its inference model can instantly identify intervention targets for new, unseen samples with different interventions and causal graphs, all without the need to retrain or refer to observational data. Specifically, leveraging the principle that interventions entail the removal of all incoming edges intervention targets, our VGAE framework is designed to estimate the probability of edge removal for each node, thereby identifying the intervention targets. The generative model within the VGAE employs non-linear Graph Neural Network (GNN), an extension of linear SCMs that adheres to causal factorization and meets the criteria for causal interventions, across causal graphs of various structures and sizes. This theoretical foundation ensures robust ITE capabilities. The generative model accepts exogenous noise variables intervention
u, the adjacency matrix of the observational causal graph A, pernoulli-distribute indicator 7 characterizing edge removal probability, and outputs the distribution of endogenous variables x. Conversely, the inference model interprets a given sample of endogenous variables x to infer distributions of exogenous variables u and the intervention indicator y, all of which rely on the causal graph A. We employ graph attention networks (GAT) as the backbone due to their flexibility and scalability. This VGAB--comprisin in a self-supervised manner by maximizing the evidence lower bound (ELBO) or can leverage labeled intervention targets when available for semi-supervin supervised learning.
both the generative and inference models be trained
Our contributions can be summarized as follows:
We propose novel VGAE architecture tailored for ITE, termed DeepITE. It excels in collaborative learning from varying causal structures and interventions, negating the need for retraining with each new instance.
We establish self-supervised and semi semi- supervisd training approaches for DeepITE, allowing it to autonomously discern intervention targets and enhance accuracy through the integration of labeled data from controlled experiments.
'e detine an intervention instance as one manipulation on the causal system. We can then collect set of data with the same intervention targets for this instance.

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Extensive experiments show that DeepITE surpasses 13 baseline methods by large margin on average in terms of Recall@k with competitive inference time, especially for large graphs.
2
Related Works
In this section, we briefly review the literature on ITE. Moreover, we notice that the realm of ITE is interconnected with causal explanations and RCA. These three concepts demonstrate a considerable degree of overlap (cf. 88 4 9. suggesting that methods from each domain can not only inform and enhance one another but also be utilized in a complementary fashion. We therefore refer the readers to Appendix B for further discussion on causal explanations and RCA.
The limited literature ITE approaches bifurcates, with one camp focusing on incidental estimation of intervention targets via causal discovery and the other dedicated solely to identifying intervention targets within given causal framework. The former includes methods such as UT-IGSP [00, which seeks to recover an interventional Markov equivalence class (I-MEC) through permutation searches but hampered by sample inefficiency and limited scalability. Ghassami et al. [I]] explore linear structural causal models (SCMs) yet may struggle with complexity in diverse data settings. For causal insufficient systems, Jaber et al. [2] propose V-FFCI for matching interventional distributions to causal graphs and intervention target pairs, contending with exponential growth in complexity as the number of variables increases. Mooij et al. [13] alternatively propose a method leveraging context variables for integrating interventional datasets, but this method suffers from scalability issues with large graphs. To overcome this problem, RCD [9] further adapts the V-FCI algorithm in 12) to the V-PC algorithm to expedite the process in causally sufficient systems. The second approach, exemplified by CITE 3 and PreDITEr [6, zeros on ITE by contrasting precision matrices from observational and interventional data, achieving scalability at the cost of being initially restricted to linear Gaussian SCMs. LIT 7 explores non-linear SCMs through non-linear ICA, still carrying quadratic complexity. Alternatively, CI-RCA 4 conducts ITE by detecting shifts in probability distributions of a variable conditioned on a variable's 's parents via hypothesis testing. Both groups of methods share critical disadvantages: they are vulnerable to even minor changes in data or causal graphs during both learning and inference, and they underutilize labeled data from controlled experiments.
3 Preliminaries
This section lays the groundwork for our study by introducing SCMs and Pearl's Causal Hierarchy,
Structural Causal Models (SCMs): Given a set of variables x = [x,,..., xu), SCMs present a formal mechanism represent causal relations among them. An SCM composed of two primary components: set of structural equations and a causal graph. The structural equations take the form: X == fi (Pa(x)) uz), i =,2, m,
(1)
where f. is deterministic function, Pa(x,) denotes parent variables that exert direct causal influence on x,, and u, signifies unobserved exogenous variables capturing influences not represented by other variables in x. We typically invoke causal sufficiency, assuming the u, are jointly independent, thereby ruling out hidden confounders. The use of the assignment symbol "==" instead of an equality sign underscores the asymmetry of the causal relationship. The corresponding causal graph G (see Figure [ a for an example) induced by the SCM is typically a directed acyclic graph (DAG) with vertex set U u and directed edges from each variable on the right hand side (RHS) of structural equation (T) to the variable on the left hand side, thus delineating the causal dependencies.
Pearl's Causal Hierarchy (PCH): Under the SCMs framework, PCH categorizes causal inference into a three-ticree structure reflective of the cognitive processes of "sccing" " (observational), "doing" (interventional), and "imagining" (counterfactual), The initial tier addresses observational queries using SCMs as conventional probabilistic models to describe statistical associations.
Progressing to the second tier of PCH, SCMs distinguish themselves from standard probabilistic models by enabling the assessment of outcomes resulting from active interventions or manipulations, captured by the notions of do-operatar and graph surgery. Here, the do-operator do(x; = x;)) repre- sents an intervention that sets the variable x; to value vi, while graph surgery alters the corresponding causal graph by removing all incoming edges to the intervened-upon variable x, Interventional queries are then addressed by performing probabilistic inference in the modified graph, which often reveals new conditional independencies due to the excision of edges. For example, the interventional
3

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Supervised Loss
A + A
Latent Space
Inference
Generative Model
Model
GAT
GAT
Graph
(a) Observation Graph before Graph Surgery
Surgery Ay
q(rlx,, A)
Linear Decoder
GAT
GAT
GAT
GAT
MLP
MLP
quu x A)
GAT
GAT
Pooling
3
Observation Noise
(5) Intervention Graph after Graph Surgery
q(C(x
Self-Suuervised Loss
(c) DeepITE Architecture
Figure 1: Left Panel: Illustration of the do operator and the corresponding graph surgery: (a) The observation
graph G: (b) The intervention graph 97 for do(x2- = .2). Right Panel (c): The DeepITE architecture has an inference and a generative model. The inference model uses three-branch GAT to link endogenous variables x to postcrior distributions of intervention indicators ",,, exogenous variables u, and observation noise precision Ç. The generative model then synthesizes x given these latent variables following Eq. 7 plus observation noise
distribution p(x3 dx = x2)) for the SCM in Figure [b) is obtained via probabilistic inference with regard to (w.r.t)) the intervention graph:
p(xal do(x2 = r2))
Σ p(x (xxaxxxx x2),
(2)
contrasting with the conditional distribution in the original graph (i.e., Figure [
p(xs X22 = 22) Σ p(x1|x2 = x2)p(xx|X1,x8 X2 x2))
x1
The key distinction is the marginal p(x1) in versus the conditional p(x |x2 x2) in (3. reflecting that the causal relationship between X1 and X2 is broken by the intervention do(x; = z2)-
Finally, there are counterfactual queries about what would or could have been, given that something else was in fact observed. We refer the readers to 14 as this topic beyond the scope of our paper.
4 Problem Formulation
As discussed in the introduction, intervention target estimation ka. intervention recognition) is pivotal component within the landscape of causal analysis, addressing the question of which nodes within given causal system should be subjected to intervention in order to best explain the given interventional data. This inquiry draws upon the framework established by SCMs and operationalizes the principles enshrined in the second tier of Pearl's Causal Hierarchy. More formally.
Definition 1. Given a causal graph G with variables x, the observational data, and the interventional data corresponding to certain intervention targel estimation is to identify X1-
subset of variables X7 C x, the task of intervention
Here, we maintain the assumptions causal sufficiency and the acyclicity of the causal graph. Note that while the DAG structure (i.e., the adjacency matrix A) is assumed to be known, the explicit forms of the structural equations remain unspecified. In comparison with the interventional queries mentioned in Section 3 which presuppose knowledge of where interventions have occurred and seek to determine their effects, ITE is the inverse process: starts with the consequences of interventions and works backward to identify the sources of these perturbations. Essentially, are solving for the origin the observed interventional data, rather than predicting their impact.
In this paper, we innovatively solve the problem of intervention target estimation from the perspective of graph surgery. Recognizing that the interventional data align best with the intervention graph (see Figure (bb), our objective is to discover the subset of nodes Xy such that, upon hypothetically removing the incoming edges to these nodes, the modified interventional model most accurately reflects the presented interventional data. Furthermore, We aim to create a singular model capable of pinpointing distinct sets of intervention targets for individual samples, each associated with unique causal graph, while eliminating the need for observational data during inference. This represents

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significant shift from conventional ITE methods that independently identify shared intervention targets for each intervention instance based on both observational and interventional data within the context of fixed causal graph. Additionally, we seek to enhance model performance by incorporating labeling information during the training phase, which is a step forward in refining ITE processes.
5 DeepITE
To move forward to the above objectives, we present DeepITE, a VGAE that can estimate the probability of edge presence within the latent space. The architecture of DeepITE is showcased in Figure [c.. We will first introduce the generative and inference models within the VGAE. Subsequently, we will explicate the self and semi-supervised methods to train the inference model.
5.1
Generative Model
The chief aim of the generative model is to recreate the observed variables x given the exogenous noise variables u, the adjacency matrix A of the causal DAG, and set of nodes I that have been intervened upon. Notably, when I is an empty set, the model capable of recovering the observational distribution.
Specifically, when the structural equations are linear, they can succinetly written as x AA''x u. Therefore, given and A, we can derive x through a linear decoder:
X - ) -'m u.
(4)
Drawing inspiration from DAG-GNN
15
we extend this formulation to non-linear scenarios with: f2((1 - --)='
(5)
where Si and f2 are non-linear, component- wise learnable functions. In practice these functions are executed by MIPs, which serve as universal approximators. Assuming f2 is invertible, the aforementioned decoder corresponds to conglomerate of non-linear structural equations [15]
+ 52 (x) = A" (x) (u))
This setup implies that when fi and / are suitably expressive, they can transform u and x into a space where their causal interrelations are aptly described by linear structural equations. The decoder, as specified in Eq. (5) exhibits inductiveness, enabling generalization to new nodes, edges, or graph schemas by only modifying the adjacency matrix A while preserving the learned functions fi and fz- This decoder displays a particular characteristic, ratified by the following proposition:
Proposition 1. For a GNN layer as defined Eq. b and denoting An(i) as the ancestor nodes of node i with the extension An" (i) An(i) each output feature x; exclusively acquires information from its own and all ancestor input features UA) Proof. See Appendix C
Owing to this property, this decoder (5) satisfies causal factorization and captures causal intervention, as proven below.
Proposition 2. (causal factorization) The decoder defined in Eq. (5) conforms to causal factorization p(x|u, A) = II p(x; (a)) that is, each endogenous variable X; can be expressed as a function of its exogenous variable u, and those of its causal ancestors.
Proposition 3. (causal intervention) The decoder defined in Eq. captures causal interventions do(xı = æτ) by replacing the original adjacency matrix A in Eq. with the one corresponding to the post-interventiin graph after graph surgery.
Proof. See Appendices and E
As established in Section 4 ITE resides within the second tier of PCH. Within this framework, any model purporting to tackle the ITE challenge must adeptly manage both observational and interventional data. The above propositions bridge this requirement, affirming DeepITE's competency in fulfilling the ITE task. Specifically, these propositions serve as the key to unlocking the model's ability to honor the causal structure inherent in the data and to emulate the effects of interventions.
In light of Proposition 3 to manipulate the intervened nodes I in the decoder 5) we introduce Bernoulli distributed variable %; for each node x;: 7: means x; intervened, and thus, all incoming edges of x; is removed during the graph surgery. The variable 7; is henceforth referred to as the intervention indicator. The corresponding intervened adjacency matrix given by Az (TT1) o A, where 1 is a column vector of all ones and @ denotes Hadamard product. As a result, decoder, inclusive of interventions, is delineated as:
x Dec(u, u, 7, A) = f2 ( A7) --(l)))
(7)
5

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When Y/
for all i, there is no intervention (i.e., Ar A) and the above decoder can describe the observational distribution.
To facilitate the reparameterivation trick in VAE, we assume the exogenous variables u are standard normal distributions: u, .V(0,, 1). Finally, since we do not have access to the true structural equations, introduce the observation noise (O, ( to 77. so to account for the model uncertainty associated with the above decoder (7). Here, Ç denotes the inverse variance of the noise,
and we impose non-informative Jeffrey's prior on Ç. that is, p({) 1/C.
Collectively, the overall generative model can be factorized as:
m
p(x, u, 7, ((A4 = p(x|u,
(p(() II p(u))(())) i=1
(8)
where
p(u,) = 1),, p(7)) = Bern (T) Vi,
p(() 1/C, p(x|u, 7, A) (De((e,, 7, 1)
(9)
Here, denotes the probability of taking in Bernoulli distribution and I is the identity matrix.
Hard interventions, characterized by the removal of incoming edges as part of graph surgery, contrast with soft interventions, which modify the causal mechanism without complete elimination. For instance, for an intervened node X,, a soft intervention would replace the original structural equation == [<(Pa(x,) u,) with an updated version x; == f(((a())): u.), where f. * f/. thereby altering the generative process while maintaining the graph's s structure. In our generative model, the intervention indicator Y is Bernoulli variable, allowing the learning of edge removal probability from data x. A probability of y; = being one indicates a hard intervention, whereas any other value suggests soft intervention. This nuanced approach allows DeepITE to offer a spectrum between hard and soft interventions based on given data.
Discussion on Hard versus Soft Interventions:
5.2 Inference Model
The pinnacle goal of the inference model within DeepITE is to determine the probability that node i has undergone an intervention based on the observed data, succinetly expressed as p(7: = 1|x). To achieve this, we aim to compute the exact posterior p(u, 7 Ç|x, A). However, the intricate nature of this posterior necessitates approximation through tractable inference model q(u, 7, < A) [66 Specifically, the inference model can be factorized in manner akin to the generative model as:
quu, 7. Ç\x, A) = o((]x,
x. A) II ( x. A)g(T.lx.A) A).
(10)
The variational distributions on RHS parametrized by: q(u,/xx A) = N (H, A), of (x, A)),
(11)
((, xx A) Bern (w, (x, A)),
(12)
<[||x, A) = .oognormal (pc(x, A), of A));
(13)
where the Bernoulli distribution (26) can be well approximated using the Humbel-Softmaal reparam- eterization trick [8 19]. The parameters of the variational 9 distributions in (25 27 are derived from a network based on Graph Attention Networks (GAT). While any inductive spatial GNN can be used the inference network in DeepITE, we choose GAT since provides the flexibility and scalability necessary for our model. This flexibility stems from GAT's ability to dynamically weigh the importance of different nodes, thus allowing the variational distribution given by the inference network better approximate the exact posterior distribution. This advantage is further demonstrated in Appendix G.6 where we replace the GAT encoder with the encoder of DAG-GNN.
The architecture features an initial dual-layer GAT for feature extraction, followed by three specialized branches dedicated to the parameters of 7, and Note that the parameters of and y can be regarded as node-level features, while those of ( as graph-level features. As such, the final Ç branch includes pooling layer to yield the graph-level features. The complete inference network is depicted in Figure C.).
It is pertinent to mention that the graph associated with the GATs is undirected, in contrast to the directed nature of the causal graph. This design choice motivated by the need for the variational update of a variable to account for all elements within its Markov blanket, which includes the parents,

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children, and co- co-parents of the node (20). An undirected graph facilitates the message passing process within this Markov blanket by removing the constraints imposed by edge directionality. Moreover, the use of GAT ensures that the resulting model is inductive, enabling its application to new nodes within the graph and even entirely new graph structures.
Relation to DAG-GNN [15]: DAG-GNN's objective is to infer the structure of a DAG (i.e., the zero pattern of A) from the provided data, process known as causal discovery. The architecture of DAG- GNN employs a generative model expressed as x = (2( - )") fi (u)) and an inference model as u= - A ) fs((x)) both of which are differentiable with respect to A. This differentiability is crucial as it enables the learning of A through gradient descent. On the other hand, DeepITE enhances the generative model by integrating an intervention indicator, which facilitates the adaptation of the model to account for interventions via graph surgery. Furthermore, DeepITE's s inference model seeks to closely approximate the true posterior p(u, 7. (|x, A). Unlike DAG-GNN, which may only collect messages from the parents of a node, DeepiTE'ss model is designed to aggregate messages from all nodes within the Markov blanket of given node i. This comprehensive approach ensures that DeepITE's inference model is not as restrictive DAG-GNN's and is better suited for ITE tasks.
Relation to VACA 211: VACA sets out to perform causal queries utilizing observational data within the framework of the VGAE. It hinges on Message Passing Neural Networks (MPNNs) for both the generative and inference models. A prerequisite for VACA to perform observational and interventional queries is that the generative model's 's number of MPNN layers must at least be 8 - 1 given that 8 is the graph diameter This criterion ensures that the information propagated within the graph can reach from one end to the other, therehy reflecting the global structure necessary for accurate causal inference. DeepITE aligns with this requirement for effectively performing ITE. However, DeepITTE distinguishes itself by employing the generative model, specified in Eq. 77. It keeps the number of GNN layers to be one regardless of graph diameter, while satisfying causal factorization and intervention conditions (cf. Proposition 3). DeepITE thereby overcomes the limitations imposed by VACA's dependence on graph diameter, offering a substantial benefit for collective learning on graphs with different sizes.
5.3
Self and SuperSipee Learning
DeepITE's learning strategies encompass self-supervised learning, which automates the identification of intervention targets from unlabeled data, and supervupe learning, which refines the model's performance by integrating labeled data. The training process is summarized in Algorithm TT
Self-Supervised Learning: Given the generative and inference model, we can learn their parameters jointly by maximizing the evidence lower bound (ELBO) of the log- ligelioel of the given data x:
(14) where Eg denotes expectation over the q distribution in (10) and Hl denotes the entropy of the distribution. The derivation of the ELBO can be found in Appendix Note that L can be maximized
f == E., p(x, u, 7: A)} + IHT, S log p(x A),
via stochastic gradient ascent after using the reparameterization trick for normal and Bernoulli
distributions 22 23 19]
Semi-Supervised Learning: Information regarding intervention targets may often be available in practice. For instance, in the case of RCA in cloud-native systems, the ground truth of intervention targets can be derived from resolved incidents and chaos engineering exercises. This ground truth data can be utilized to train the inference network, enabling it to more accurately identify intervention targets. In particular, the term 4(7, x, A) is replaced by the ground truth 73 when computing the ELBO L, and an additional term is introduced to maximize the log- likelihood log 4(>; [x, A). By taking advantage of both labeled and unlabeled data, we can effectively train the inference model.
Once trained, the inference model of DeepITE becomes equipped to evaluate individual new samples against different causal graphs, directly deducing the intervention targets and thus circumventing the necessity of retraining for each new scenario. It can also distinguish between observational and interventional data directly as the former equates to the absence of intervention targets. Another significant feature of DeepITE is its independence from observational data during the testing phase; it relies solely on the interventional data input into the inference network. This is a distinct advantage over existing ITE methods, which consistently require observational data for ITE. In practice, we are primarily concerned with the intervention indicators 7. Hence, during inference, we only need
'The diameter of graph is the length of the shortest path between the most distanced endogenous nodes.
7

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Table I: Recall@@ k of different algorithms for detecting the intervened nodes from the Synthetic dataset. Graph-m means DAGs with nodes.
DATASET
Graph-50
Graph-100
Graph-500
METRICS
Recall@ 1
R
Recall@1 1
Recall@
Recall I
Recal
UT-IGSP
0,224 + 0,015
0.318
0.019
0.079 + 0.007
0.185 = 0.010
0.016* + 0.002
0.020 1 0.004
CITE
0.098 + 0,009
0.124
0.013
0.044 = 0.003
0.063 = 0.006
0.007 = 0.001
0.008 + 0.001
PreDITEr
0.104 + 0.009
0.122
0.012
0.049 = 0.004
0.066 = 0.006
0.008 0.001
0.008 + 0.001
Treelxplainer
0.381 +: 0.022
0.510 0.017
0.298 0.016
0.448 0.009
0.102 0.008
0.152 1: 0.002
ASV
0.296 +: 0.021
0.390
0.022
0.261 0.014
0.323 + 0.017
0.081 0.003
0.140 0.005
ShapleyFlow
0.552 0.017
0.690
0.009
0.378 0.009
0.485 = 0.007
0.124 0.005
0.148 0.002
PWSHAP
0.468 0.014
0.610 0.012
0.339 0.009
0.454 0.008
0.117 0.003
0.195 0.003
CauseInfer
0.561 0.002
0.765
0.003
0.554 0.002
0.786 + 0.02
0.559 0.003
0.769 0.004
MicToHECL
0.462 0.010
0.587 0.009
0.341 0.004
0.400 0.004
0.199 0.003
0.241 0.004
MicroRCA
0.647 0.004
0.899
0.003
0.623 0.004
0.875 + 0.004
0.436 0.003
0.676 0.003
CausaIRCA
0.633 + 0.004
0.894 = 0.004
0.622 0.004
0.863 + 0.004
0.418 0.004
0.630 + 0.004
CI-RCA
0.615 0.002
0.952 0.001
0.631 + 0.002
0.930 + 0.003
0.623 0.004
0.823 + 0.003
RCD
0.495 + 0.003
0.706
0.004
0.440 0.004
0.521 + 0.005
0.325 0.002
0.364 0.003
DecpITE (scp)
0.723 = 0.002
0.972
0.003
0.685 = 0.004
0.968 = 0.002
0.642 = 0.003
0.891 = 0.004
DeepITE (mix)
0.718 + 0.003
0.945 0.005
0.690 + 0.004
0.923 = 0.003
0.627 = 0.003
0.875 + 0.004
to process x through the relevant branch of x, as highlighted by the red dashed box in Figure Tc), disregarding the other branches to optimize inference efficiency.
6 Experimental Results
In this section, we demonstrate the usefulness of DeepITE on three datasets, comprising one syn- thetically generated dataset, which provides a controlled environment to test the robustness and scalability of the framework, and two real-world datasets that introduce the complexity of genuine causal systems. We position DeepITE against 13 state-of-the-ar (SOTA) methods, spanning three areas of relevance: Intervention Target Estimation (ITE), Explainable AI (XAI), and Root Cause Analysis (RCA), due to their intertwined nature (see more discussions in Section P and Appendix BB)
ITE: We select 3 methods: UT-IGSP 10]
which learns intervention targets as a byproduct of causal discovery; CITE 3 and PreDITEr both of which are dedicated to ITE
XAI: We opt for TreeFxplainer 24 ASV
25 ShapleyFlow [26]. and PWSHAP (27 4 methods based on Shapley values. TreeExplainer only considers associations, whereas ASV, ShapleyFlow, and PWSHAP incorporate causation, accounting for the DAG structure.
RCA: We pick 6 methods: CauseInfer (28), MicroHECL 229,, MicroRCA [30), CausaIRCA BI).
CI-RCA [4], and RCD (9]. The last two aim to find intervention targets in a causal graph.
To facilitate fair comparison, all methods are provided with the same ground truth causal graph, eschewing the need for graph construction from data for some RCA methods. More implementation details can be found in Appendix G.I] The performance is quantified using the Recall @ k metric. Recall@k measures the proportion of true intervention targets (ITs) that are successfully captured within the top k ranked candidates proposed by cach method. This metric is widely adopted in the literature 44] g (2)]. When h = 1, our goal is to pinpoint the intervention targets based on the highest-ranked candidate. We prioritize Recall@k because, in practice, false positives can be eliminated through further analysis, while false negatives are irrecoverable as they get lost among the numerous true negatives. All experiments report average results over 10 trials, with error bars representing a standard deviation (o) from the mean.
Synthetic Data: The synthetic data is generated following the method outlined in CI-RCA 4 and in Appendix G.2 We assess causal graphs with nodes ranging from 50 to 500 and corresponding edges from 100 to 5000, exhibiting different levels of complexity. In particular, DeepITE is evaluated in two configurations: DeepITE (sep), where separate models are trained for each graph size, and DeepITE (mix), where single model is trained across all graph sizes and structures.
Analysis of the results in Table I reveals 5 key insights: (i) Traditional ITE Methods Fall Short: Such methods underperform with larger graphs with fewer samples due to their design for small, dense datasets (typically involving tens of nodes but with tens of thousands of samples) and lack of cross-instance learning, treating each intervention instance in isolation. As shown in Appendix G.3 they perform much better for small graphs with large sample size. (ii) XAI Methods Face Challenges: TreeExplainer lacks consideration for causal relationships in graphs. ASV and Shapley Flow, although
8

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Table 2: Results of the Protein Signaling Data and the ICASSP-SPGC Data.
DATASET
Protein Signaling
ICASSP-SPGC 2022
METRICS
Recall@1
Recall 1
R
Root.Acc
Score
UT-IGSP
0.579 + 0.018
CITE
0.588 + 0.011
PreDITEr
0,586 + 0.010
TreeExplainer
0.434 = 0.020
0.367 = 0.013
0.687 + 0.010
0.7401 = 0.0153
0.3534 = 0.0298
ASV
0.441 + 0.017
0.449 + 0.010
0,720 + 0.008
0.7933 # 0.0117
0.3820 + 0.0230
ShapleyFlow
0,615 + 0.021
0.677 + 0.013
0.825 + 0.015
0.9176 + 0.0131
0.5312 + 0.0252
PWSHAP
0.603 + 0.016
0.488 + 0.009
0.741 + 0.010
0.8551 + 0.0109
0.4233 + 0.0211
Causelnfer
0,076 = 0.002
0.278 = 0.003
0,490 + 0.001
0.5808 + 0.0084
0.1139 + 0.0180
MicroHECL
0,081 = 0.004
0.323 =:23=00 0.010
0.661 + 0.011
0.7339 = 0.0134
0.3697 = 0.0283
MieroRCA
0.127 + 0.003
0.246 0.004
0.463 - 0.002
0.5662 + 0.0089
0.0721 + 0.0204
CausalRCA
0.113 + 0.001
0.308 + 0.004
0.447 + 0.003
0.5353 + 0.0078
0.0617 + 0.0161
CI-RCA
0.090 + 0.001
0.559 0.002
0.828 + 0.001
0.9284 + 0.0055
0.5650 + 0.0105
RCD
0.214 + 0.002
0.481 0.008
0.757 + 0.005
0.8768 + 0.0112
0.4542 + 0.0216
DecpITE
0.652 + 0.002
0.881 0.002
0.984 + 0.000
0.9794 + 0.0023
0,9085 + 0,0040
aware of the causal structure, struggle with scalability similar to traditional ITE methods, as these methods demonstrate optimal performance on comparatively smaller graphs (around 10-20 nodes)
(cf. 125 26 ). (iii) RCA Methods Show Promise but Have Limitations: RCA methods generally perform better than ITE and XAI approaches, with CI-RCA aligning exactly with the ITE task. However, these methods also face challenges in integrating multiple intervention instances effectively. (iv) DeepITE Models Excel: Both DeepITE (sep) and DeepITE (mix) outperform all benehmarked methods on Recall@1 1 and Recall @5 metrics, attributing success to a flexible model that fosters collaborative instance learning, independent of graph characteristics, enabling precise alignment of data with intervention targets. (v) Competitive Performance within DeeplTE Models: The two DeepITE models (sep&mix) demonstrate competitive results, indicating that the model's inductive strength and its adaptability to various graph structures and sizes.
The description of the dataset is presented in Appendix G.4 The results, shown in the second column of T focus on the Recall I metric due to the graph's s limited size. It is evident that DeepITE outshines competing methods, attributable to its capacity for collaborative learning across the entire dataset and its inherent adaptability. Traditional ITE and XAI methods trail closely behind, with their methodologies being particularly suited to smaller graphs that nevertheless have substantial number of samples. In contrast, RCA methods exhibit weaker performance as techniques such as PageRank, DFS, BFS, or rank walk struggle to distinguish between nodes such a compact network. Unlike these approaches, DeepITEE demonstrates versatility in handling both small and large graphs, affirming its utility across a wide range of practical scenarios.
Protein Signaling Dataset:
ICASSP-SPGC 2022: The details of this dataset can be found in Appendix G.4| Note that the absence of purely observational data in this dataset precludes the application of the ITE methods including UT-IGSP, CITE. and PreDITEr. For our evaluation, we continue to employ Recall @ I and @ metrics and additionally consider accuracy and the root-cause score, the latter two being an official recommendation. The root-cause score is calculated by subtracting the number of false positives from the number of true positives and then dividing by the total number of true intervention targets. The results of this multifaceted assessment are presented in the last four columns of Table 2 Once more, in comparison with the SOTA methods, DeepITE stands out by a large margin of above 20% in Recall@ I, underscoring its applicability to real-world RCA challenges. Notably, CI-RCA and Shapley Flow emerge as close second-best performers, likely because both methods leverage causal rather than just correlational information, which appears to be advantageous in this context.
6.1 Ablation Study
Due to the page limit, we only present an overview of the major findings here. More details can be found in Appendix (.55G.77 (i) Impact of Label Proportions: Incorporating even modest amount (5-10%) of labeled data significantly enhances DeepITE's performance across various datasets, with recall improvements ranging from 4-20%. This approach provides substantial benefits in practical settings, unlike other baseline methods in the study that cannot utilize labeled data at all. (ii) Replacement of Encoder and Decoder: Modifying DeepITE's encoder to DAG-GNN's and its decoder to VACA's in two ablation designs shows that DeepITEE-wihh its flexible generative and inference noupers-uu both modified versions and the original VACA 21 and DAG- GNN [15). These observations, especially under conditions of increasing graph complexity, highlight

---

1.00
0:25
0.75
0075
4.75
0.75
0.50
1030
Siaph 5204
Intervertioos
numburs
Gooh 1200
(a) Graph sizes
(b) Interventions
(c) Mixed hard&soft (d) Graph samples
(c) Mixed graphs
Figure 2:
The performance of DeepITE as function of (a) graph sizes, (b) interventions, (c) the mixture
proportion of soft and hard interventions, (d) sample size for each graph, (e) the number of mixed graphs.
DeepITE's robustness and the limitations of DAG-GNN's rigid design and VACA's requirement of minimal decoder layers in estimating ITE. (iii) Scalability: Figure aa-b) revealed the performance of DeepITE function of the graph size and the number of interventions respectively. Our findings indicate that while performance in terms of @ecall I declines as the graph size increases, @ remains stable even for graphs with 1000 nodes-a size that is already considered quite large for causal analysis. (iv) Hard&Soft Intervention: We examined DeepITE under varying ratios of hard and soft interventions, where soft interventions were modeled by modifying the linear structural equations of the intervention targets to quadratic forms. Figure ((c) shows DeepITE's adaptability to these mixtures, confirming its effectiveness in handling both types of interventions with only minimal performance loss. (v) Samples: Figure illustrates the performance of DeepI'TE with variations in sample size. Our results show that DeepITE exhibits robustness with performance generally improving as the sample size increases. In contrast, traditional ITE methods S G, [0] typically require thousands of samples for single graph and intervention set to perform well. This resilience can be attributed to the collaborative learning framework of DeepITE and the relatively few parameters in the GNN-based encoder and decoder. (vi) Amortization: The performance of DeepITE as more graphs with different sizes are trained together, is detailed in Figure [(e). Our findings indicate a minimal gradual degradation in the performance of DeepITE (mix) as we incorporate more graphs of varying sizes, attributed to the amortization error. Moreover, Table [ shows that DeepITE (mix) even outperforms DeepITT. (sep) training exclusively on 100-node graphs terms of Recall @ 1, Based on this evidence, we maintain that the amortization process across graphs does not significantly hinder the performance.
6.2 Runtime Analysis
We conducted a runtime analysis using four distinct datasets, with variable counts m ranging from 5 to 500, specifically m = [5. 10, 11, 20, 50, 100, 500). For each value of m, we executed 10 trials on a set of 1000 samples and reported the average runtime. To ensure fair comparison, we focused exclusively on the code pertinent to intervention identification. Timing commenced the moment the algorithm received the dataset and accompanying causal graph, if applicable, and ceased immediately upon delivery of the final results. This process ensured that our analysis exclusively measured the performance of the algorithm's 's core intervention-targeting functionality.
The runtime performance of the various methods, relative to graph size, is depicted in Appendix Figure 3 From the analysis, we note that DeepITE's runtime curve, represented in black, has the gentlest slope, implying that it boasts the lowest time complexity among all the methods. Notably, DeepITE secures the shortest runtime for graphs with more than 100 nodes. This heightened efficiency attributable to DeepITE's inference process, which necessitates only single pass through one branch of the inference network. In contrast, UT-IGSP exhibits the highest time complexity as it engages in an exponentially growing number of hypothesis tests to identify intervention targets. For instance, when handling graphs with 500 nodes, UT-IGSP requires nearly an hour to complete a single run.
7 Conclusion
In this paper, we presented DeepITE, a novel VGAE for ITE. By carefully design the VGAE based on GNNs, DeepITE allows collaborative learning and amortized inference across data with a range of intervention targets and causal graphs. The model adeptly supports both self-supervised and semi- semi-supervised learning modalities, effectively harmessing labeled data to refine ITE. Our comprehensive results demonstrate that DeepITE can be seamlessly adapted a multitude of domains, accommodating diverse causal graph configurations while exhibiting superior performance in terms of both Recall@t k metrics and computational efficiency.
10

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Acknowledgements
We would like to thank Ant Group for their support for this work.
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Table 3: Notations and their meanings.
Notation
SIZE
Meaning
g
causal graph
A
the asymmetric adjacency matrix of observational DAG
endogemous variables
exogenous variables
An(
the ancestor nodes of node
(A)
exogenous variables of the ancestor nodes of node do-operation
do(x; xz2)
I
of the intervened nodes the adjacency matrix of interventional DAG column vector of all
Az
1
Hadamard product variational q-distribution
(()
p())
variational p-distribution
N
normal distribution
Bern
Bernoulii distribution
Lognormal
log-normal distribution
latent variables for normal distribution
7
latent variables for Bernoulli distribution
1x1
global observation noise
1 1x1
inverse variance of the noise mean of normal distribution of node
A4
1x1
of
1x1
variance of normal distribution of node
1x1
probaility of taking in Bernoulli distribution mean of log normal distribution
P
of
variance of log normall distribution
(x A)
1x1
mean of the estimated normal distribution of node i variance of the estimated Normal distribution of node
07 A)
1x1
wi(x, A)
1x1
estimated probaility of taking
Bernoulli distribution of node mean of the estimated log normal distribution
A)
1x1
A)
1x1
variance of the estimated log-normal distribution
p(x, u, U.
the proposed gernerative model the proposed inference model expectation the y-distribution entropy of the q-distribution the evidence lower bound (ELBO) KL divergence between distributions and the proposed decoder
q(u, c. B
HI
C
DKL (allp)
Dec(u, 7,
A
Notations
See Table 3
B
More on Related Works
Causal Explanations: We also briefly review causal explanations as they are related ITE. AI explainability endeavors to demystify model decisions, a pursuit encompassing feature attribution and contrastive explanations. Feature attribution methods initially focused on associations, revealing how input features correlate with predictions 24 Moving beyond mere correlations, recent approaches integrate causality to enhance interpretability: CXPlain [32] employs supervised learning to discern the causal impact of features model predictions, though computational demands escalate with
15

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the need for repeated model evaluations. Generative Causal Explanations (GCE) 33 introduce disentangled latent factors to isolate causal effects, yet ensuring these factors accurately reflect the
data distribution is complex. Asymmetric Shapley Value (ASV) 225 Shapley Flows [26], and
PWSHAP [27] further this trend by considering the causal graphs, with the former adapting Shapley
values to reflect causal structures and the latter two focusing on the causal relationships signified by the graph's 's edges and paths. On the other hand, Contrastive explanations. illustrated by Counterfactual
Explanations (CE) (3 and Causal Algorithmic Recourse (CAR) [35] 36), offer a counterfactual narrative on how slight modifications or specific interventions could lead to different, often more favorable outcomes. Specifically, CE suggests minimal feature changes for an alternate outcome, teaching individuals how to achieve a different result. Their application must navigate constraints of plausibilityy and actionability to avoid recommending impractical changes. CAR extends CEs by proposing interventions grounded in causal relationships, aiming to recalibrate outcomes with consideration of the cost and effect of actions.
These causal explanation methods intersect with ITE. They can serve as foundation for ITE by treating the most influential factors as potential intervention targets. Conversely, ITE can reciprocate by informing causal explanations since it pinpoints the very intervention targets that are the roots of observed outcomes.
Root Cause Analysis: Lastly, graph-based RCA methods warrant discussion, due to the intertwined nature of root causes and intervention targets. These methods usually operate in two distinct stages. Initially, the graph structure is established either via causal discovery algorithms such as
the PC algorithm [28 37 38
39 40
42
43 Granger causality [44] and DAG-GNN
31. or
it derived from domain-specific knowledge like topology graphs
45
46
47
4
49
30
The second stage then leverages algorithms such as PageRank [46]
breadth-fire first or depth-
first search [28 37 38 29], and random walk (50) 40
42
43
30
for root cause localization
within the graph. Despite their utility, the second stage tends to focus on association rather than causation, assigning a higher correlation to the connections between a node and its parents over those between the node and its children to consider the directionality in the causal graph. In contrast, as highlighted in the preceding sections, CI-RCA [4] emphasizes causation, utilizing linear regression- based hypothesis testing to pinpoint intervention targets. As an alternative, RCD [9] incorporates the V-PC algorithm in a hierarchical fashion, intertwining the learning of intervention targets with graph structure discovery. These approaches not only improve root cause identification but also harmonize with ITE goals, fostering a causally informed analysis in RCA. However, as noted earlier, these methods lack collaborative learning and are fully unsupervised, necessitating complete inference for each RCA instance from scratch and failing to utilize labeling information.
C Proof of Proposition
I
According to the Neumann power series for the matrix inverse, we can obtain:
(I - -- --- E(At),.
(15)
k=
where A denotes the k-th power of the matrix A, which involves multiplying the matrix A by itself k times. In the above expression, entry (i. in the k-th power of A can be elaborated as:
(A)) =
Σ
Aum Awies
Awn-15
Wi 11.
Σ (athh
(16)
where the sum encapsulates all paths (i, W1, Wg wk--.. from to j with length k. In directed graphs, these paths must observe edge directionality. Consequently, for DAGS, the matrix A becomes zero matrix when k surpasses the graph diameter ö, paths of length k exist between any two nodes within such graphs. Substitute (15) into (5), and the GNN layer is recast as:
x
fz
(1()).
(17)
k=0
By multiplying (A with (u) as in (17). node i can receive information from its ancestors that have a path of length is connecting to i. As k goes from 0 to co, node i accrues information from the
16

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input features of its own and all its ancestors. On the other hand, paths only exist between node and one of its ancestors. Conversely, such paths are non-existent between node i and non-ancestral nodes; hence, node i exclusively assimilates inputs from its ancestors, thereby concluding the proof.
D Proof of Proposition 2
In consideration of Proposition and given the decoder in Eq. (5). is evident that x; is a function solely of (A) for all indices Consequently, the probability distribution p(x u, A) can be factorized into:
px/|u, A) II p(x:|uAn(())
(18)
which logically concludes the proof.
E Proof of Proposition 3
The essence of a causal intervention lies in severing all the incoming edges to the intervened nodes. Therefore, the decoder stipulated in Eq. (5) can faithfully represent causal interventions only if encompasses all possible causal pathways; otherwise, severing certain pathways would exert no influence on the resulting intervention configuration. The decoder in Eq. (5) does indeed model all causally relevant paths, as corroborated by Proposition [ thereby completing the proof.
F Derivation of the ELBO
Recall that the generative model (i.e., the p distribution) can be factorized as:
p(x, u, 7. (/4) p(x|u, 7, A. ()((s) Il
p(u:)p(T.).
(19)
i=1
where
p(u,) NO Vi,
(20)
p(r)) = Bern(T), Vi,
(21)
p()) 1/C,
(22)
p( 7, A, C) = =N(Dec(u, 7, A),
(23)
and the inference model (i.e., the q distribution) can be factorized as:
7(,.7, <|x, A) = q([]x, A) II gl, A)g(Tilx, A):
(24)
where
((u,/x, A) = (Mi(x, A), ,x A)),
(25)
((,, |x, A) = Bern (wi(x (x, A)),
(26)
q(5/x |x, A) Lognormal (Mę A), oxxx;
(27)
Note that the parameters of the above distributions are explicit functions of the given sample of the endogenous variables x and the adjacency matrix A, which is parameterized by the GAT-based
inference network. By substituting the p (19) and g distributions 24 into the ELBO (44.. we can obtain:
C Bg logp(x u, 7, A) + logp(C) Σ (logp(u,) + logp(T))) + Elg.
F.g logp(x 7, A) log log 9([| A)+
Σ (log log q(u x, A) + logp(%) log ((7//1,,, A))
B.
DKL(9(SIxx A) A))lp(())
17

---

DEL (a(u:)x, A))lp(u;)) + ( A)lp(())))
(28)
where DKL denotes the KL (Kullback-Leibler) divergence between two distributions. We now delve into the expectation term in the above expression, which is given by:
gg log px//u, A,A,) c)]
(log ( (5)((x Dec(u, 7, A))) Dec(u, 7, A))),
nc A) exp (nc(x, A) 02(x, A)
2
((x - Dec(u, 7, A)) (x - Dec(u, (u, 7, 7,)))
(29)
The remaining three KL divergence terms can be written as:
DEL (x(()xx A) (I)( 2 log (x;
(30)
DKL (g(ullx A)llp(u:)) (n A) (x, A) log (x, A)),
(31)
DKL A))p((7) = =wi (x, A) logit (x, A)) wi wi(x, A) logit(T)
log(1 (x A)) log(1 x).
(32)
Algorithm 1 DeeplTE Semi-supervised Training Algorithm
Require: Causal graph adjacency matrix A, endogenous variables x <(1m) labels of intervention targets (1:m] if available.
Time
Ensure: Parameters the inference network parameters of the generative network
1: Initialize é, 8 randomly;
2: repeat
Pass (12m) and through the inference network to get parameters for q(u|x (1mm) A). ((v])((1m)) .)). (2:m) A);
4: Draw samples from the Normal distribution g(u x (1:m) .A);
5: Draw samples from the Bernoulli distribution g(>/xx {(>[x((mm) (Imm), .A) using the gumbel-softman reparameteri-
zation trick;
6: Draw samples from the Log Normal distribution q(C x [1:m) A);
7:
g(1m) < Dec(u (1mm) xy (1mm) A)
8:
Time
(1am) is available then
Compute the negative ELBO and maximum log likelihood (1mm)
Tunu
10: else
11:
Compute the negative BL
28
12:
end if
Update / via gradient descent;
14:
until convergence
15:
return Φ 0
G Experiment Details
G.1 Experiment Setup
Unless otherwise specified, in all of our experiments for DeepITE, we set the hidden dimension in GAT and MLP to 64. For optimization, conducted training for 1000 epochs and select the checkpoints with the lowest training loss. The temperature for gumbel-softmax [23] is calculated by t 101 0.2e when epoch <= 500 and
used NAdam (52) with learning rate x 10-4. We
t = 0.5/(e 500) for epoch e > 500. All the training runs on NVIDIA TESLA P100 GPUs with
50GB of VRAM. All the inference runs MacBook Pro 16 inch with 6-core Intel i7 CPU and 16 GB of RAM.
The key assumptions and characteristics of the comparative methods, as well as the time complexity in the inference process, are summarized in the table 4 and To facilitate fair comparison, all methods (including ITE, RCA, and XAI) are provided with the same ground truth causal graph,
18

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Table 4: Key assumptions and characteristics of comparing methods. Here, mn and rt denote the number of nodes and edges in DAG, Pa the number of intervention targets given by the precision different estimation algorithm, and finally, T, L, and D represent the number of trees, the depth of the trees, and the number of leaf nodes in the gradient- boossed trees.
METHOD
CAUSAL GRAPh REFERENCE SET
INTERVENTION CONFOUNDER
AMORTIZATION
GRAPHESIE
TIME COMPLEXITY
DeepliTE
Goven
Sondhllzdd
No
Yes
<1000
O/m++)) +-)
UT
Unknown
Soft
O(g
CITE
Given
Require
Soft
No
No
<100
(122))
PraDITEr
Regiiee
Saft
(1
LIT
Unknown
Reguine
Soft
No
Yes
100
O(n")
Causclata
MicroHIECL.
Given
Hard
No
Yes
1030
Ots)))
Mino&CA
Given
No
Hard
No
Yes
< 1000
(kn") + Ol
CaaalRCA
O)s
CI RCA
Given
No
Hard
No
Yes
1000
Ots
Treelipphiier
Unknown
Soft
No
No
O(PLD*)
ASV
Given
No
Soft
No
No
=1000
((w")
SlupleyFlow
Soft
<100
PWSHAP
Given
No
Suft
Vos
No
100
Table Time Complexity during Inference. Here, m and n denote the number of nodes and edges in the DAG, Pa is the number of intervention targets given by the precision different estimation algorithm, and finally, T, I, and D represent the number of trees, the depth of the trees, and the number of leaf nodes in the gradient-boosted
METHOD
TIME COMPLEXITY
UT-IGSP 10
0(2m-1)
CITE
O (2*4)
PreDITEr
O (2Pa)
CauseInfer
O(m²)
MicroHECL
O(m²)
MicroRCA 30
O(m²) O(
CausalRCA 31
O(m²)
CI-RCA 4
O(m²)
RCD 9
((m²)
TreeExplainer 51
(TLD")
ASV 25
((m²)
ShaplcyFlow 26
O(m²)
PWSHAP 27
O(m))
DeepITE
O(m m)
eschewing the need for graph construction from data for some RCA methods. Within the realm of XAI, constructing forward predictive model is prerequisite for backward attribution analysis-the process used to identify the intervention targets. To facilitate this, we construct a predictive model using muddient-boosted trees. This forward model trained to predict whether the graph is intervened based on the full set of node features x, in supervised manner. During inference, we extract the intervention targets by identifying the top nodes that yield the highest attribution scores.
G.2 Synthetic Data Generation
The generation process begins by creating random adjacency matrix A, which is structured to be upper- trinn-triangllar to ensure the resulting DAG indeed acyclic. The matrix's 's non-zero entries are uniformly distributed across the range [-2, -0.5] U 00.5, 2). representing the possible strengths of causal relationships between nodes. For each node within the DAG, time series data are synthesized according model that captures causal dependencies, inspired by the linear decoder equation: x(t) = (I A (u(() + fx(( - 1)). where t indicates the discrete time steps, and B denotes the autoregressive coefficient, influencing the temporal consistency of the data.
Interventions are then introduced in the time series at time with the set of intervention nodes being randomly selected from all non-root nodes, and the size of I adhering to Poisson distribution. For nodes within I, we augment the corresponding exogenous noise, adhering to the sigma rule for significant deviation. Each generated time series spans 1000 time steps, with interventions
19

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Table 6: Recall @K of different algorithms for detecting the intervened nodes from the Synthetic dataset following the classical ITE setting.
DATASKT
Linear-10
Láncar-20
Nonlinenr-10
Nonlinear-20
METRICS
Recalle11
Recall
Recallee
Recalid
Recalle
Recallio
Recall
Recalloh3
ULJGSP
# 0:009
0,972
0.005
0.917
0.007
0.56
0.005
0.933
0.008
0.989
0.004
= 0.005
#:
CITE
0,989
0,933
0,009
0961
0.01
0.789
0.011
0.956
0.733
0.005
PreDITEr
0,944 1: 0.006
0,989
0.003
0,933 0.008
0.977
0.06
0.694
0.005
0,861
0.004
0,561 a 0,004
0.822 1. 0.003
TreeFaplainer
0.764 + 0.015
0,897
+ 0.030
0,551
0.010
0,509
0018
0.765
0.015
0,909 + 0.021
0.539 + 0.010
0.773 +: 0.017
ASV
0,751 1: 0.014
0,821 1 0.019
0.629 1 0.011
0,782 1 0.17
0,679 1 0,014
0.737 1: 0.018
0,510 1 0,010
0.554 1. 0.016
ShupleyFlow
0.858 1: 0.017
0.928 1 0.022
0.817 1 0.012
0,858
1 0.019
0.841
1 0.016
0,919 1: 0.023
0.811 1 0.011
0.870 1 0.020
PWSHAP
0.784 + 0.016
0,892
0.021
0.650
0.011
0815
1015--000 0018
0.776 0.014
0,895
0.15
0.647 + 0.010
0.781 + 0.014
Conselnfer
+ 0:001
0.935 = 0.001
0,430
0.003
0.655
0.003
0.720
0.002
0.819
0.001
=
0.692 #
MicroHECL
0.407 + 0.011
0.634 0.012
0.387
0.014
0485 0001
0,420
0.012
0.000 0.015
0,411 0.017
0:536 + 0.022
MicroRCA
0.759 + 0.002
0,890 + 0.001
0.730 0.003
01846 - 0.002
0.398
0.002
0.527 + 0.002
0,175 + 0.002
0.231 + 0.002
CausalRCA
0.820 +: 0.003
0.885 = 0.002
0,705 0.002
0.341 = 0.001
0.729 à: 0.003
0,805 = 0.003
0,545 +: 0.002
0.608 :: 0.001
CIRRCA
0.865 + 0.002
0.941 = 0.001
0.734 + 0.003
0.923 = 0.003
0.821 + 0.003
0.896 = 0.002
0.616 + 0.002
0.683 + 0.002
RCD
0.803 + 0.004
0,871 0.005
0.695 0.005
0,837 0.007
0,713 + 0.005
0.801
0.006
0.540 0.006
0.597 +
DespITE
1,000 0.000
1,000 0.000
0.998 0.001
1,000
0.999
occurring randomly between time steps 100 and 900. Data preceding the intervention time t are labeled as observational, while data from t onwards are categorized as interventional. We explore causal graphs of varying complexity, with 50, 100, and 500 nodes, and associated edge counts of 100, 500, and 5000, respectively. To ensure comprehensive assessment, 10 unique graphs are generated for each size, and for each graph, we create 100 distinct instances with varying intervention targets. The dataset is partitioned with an 85:5:10 ratio for training, validation, and testing.
G.3 Synthetic Data under the Classical ITE Setting
For an equitable evaluation with existing ITE approaches, we further devise experiments on synthet- ically generated datasets tailored to classical ITE configurations, using dataset generation scripts from VACA (21). Our setup included two linear and two non-linear SCMs. Initially, generate 10,000 observational samples from each SCM. Subsequently, we perform ten distinct interventions on randomly selected variables, mimicking the procedures followed by ITE methods. Each intervention resulted in the creation of 1,000 samples. Throughout these interventions, we maintain the SCMs' modularity and provid the causal graph with the data. We divide the dataset into training, validation, and testing batches with an 85:5: 10 ratio, respectively.
Table reveals that DeepITE consistently surpasses competing methods in performance. Notably, traditional ITE approaches like UT-IGSP, CITE, and PreDITEr benefit significantly from an adequate pool of observational samples small-scale graphs, reflecting marked improvement in their effectiveness. Similarly, XAI techniques-ShaallyyEuleee performance, proving to be well-adapted for smaller graphs where their capabilities can be fully leveraged. Conversely, RCA methods exhibit weaker results, a trend that may be linked to their design inclination towards larger-scale graphs. These larger configurations are often seen in complex environments such as microservice diagnosis, suggesting that RCA methods' strengths are not fully utilized in smaller or less complex scenarios highlighted in this comparison.
in particula--demonstaaae commendable
Furthermore, it evident that models based on the premise of linear SCMs, such as CITE, PreDITEr, and CI-RCA, tend to underperform in settings that demand an understanding of non-linear dynam- ics. In contrast, DeepITE, which utilizes learnable SCMs, demonstrates impressive versatility by effectively addressing both linear and non-linear scenarios.
G.4 Real Data Description
Protein Signaling Dataset: The well-known protein signaling dataset, which originates from Sachs et al. [53], investigates the complex interactions within T-4 cell signaling networks. The dataset comprises 11 nodes and 16 edges, with a collection of 1755 observational and 4091 interventional samples derived from five different experimental environments where various drugs were used to modulate signaling proteins. We harness an accepted ground truth network structure in (54), and the preprocessing steps outlined in [00), to benchmark DeepITE's performance with other models. The dataset is partitioned with an 85: ratio for training, validation, and testing.
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Table 7: Impact of Labeled Data on DeepITE (mix) for the Synthetic Dataset. The proportion of labeled data is shown in the brackets.
DATASET
Hcter
HelerITE-100
HeterlTE-500
METRICS
1 I
R
Rocall@
Recall @1
Recall@5
DeepITE (0%)
0.718 0.004
0.945 +: 0.001
0.690 + 0.004
0.923
0.002
0.627 = 0.004
0.875 + 0.002
DeepITE (5%)
0.820 0.002
0.986 + 0.002
0.731 + 0.003
0.960
0.001
0.667 0.003
0.922 + 0.002
DeepITE (10%)
0.821 = 0.002
0,991 +: 0.001
0.728 1 0.003
0.944 0.003
0.674 = 0.003
0,925 +: 0.002
DecpITE (25%)
0.829 0.002
0.995 = 0.001
0.737 + 0.002
0.982 0.001
0.672 0.004
0.954 = 0.001
DeepITE (50%)
0.856 0.002
0,998 1: 0.000
0,750 1: 0.003
0.996 1 0.001
0.678 0.003
0.952 1: 0.002
DeepITE (75%)
0.873 0.001
1,000 = 0.000
0.762 + 0.003
0.998
0.000
0.699 = 0.004
0.966 + 0.002
DeepITE (100%)
0.869 0.002
0.999 + 0.001
0.773 + 0.002
0.998 0.001
0.697 0.002
0.966 + 0.001
Table 8: Impact of Labeled Data DeepITE for the two Real Datasets. The proportion of labeled data is shown
in the brackets.
DATASET
Protein Signaling
ICASSP-SPGC 2022
METRICS
Recall @
Recall
R
Root.Acc
Score
DeepITE (0%)
0.652 +: 0.004
0.881 + 0.002
0,984 +: 0.001
0.9794 + 0.0054
0.9085 +: 0.0101
DeepITE (5%)
0.842 = 0.002
0.906 + 0.001
0,994 + 0.001
0.9964 = 0.0023
0.9524 0.0040
DeepITE (10%)
0.850 + 0.003
0.920 + 0.001
0.994 + 0.001
0.9964 + 0.0000
0.9524 + 0.0000
DeepITE (25%)
0.849 + 0.002
0.922 1 0.001
0,994 + 0.000
0.9964 + 0.0000
0.9524 0.0000
DeepITE (50%)
0.868 1 0.002
0.925 1 0.000
0.995 1 0.000
0.9964 1 0.0000
0.9524 0.0000
DecpITE (75%)
0.863 = 0.002
0.924 + 0.001
0.995 + 0.000
0.9964 = 0.0000
0.9524 + 0.0000
DeepITE (100%)
0.872 0.001
0.925 + 0.000
0.995 + 0.000
0.9964 + 0.0000
0.9524 0.0000
ICASSP-PPGC 20215 The ICASSP-SPGC 2022 dataset 55 derived from active 5G networks, is real-world telecommunications dataset for RCA comprising 2984 samples and 23 variables that represent various Key Performance Indicators (KPIs). Human experts have verified the accompanying causal graph, yet only around 45% of the data are explicitly labeled with root cause faults. Unlike the previously mentioned dataset, where interventions are directly known from labels, the root causes here are represented by unobserved variables outside the causal graph. However, experts provide mapping of each root cause to observable causal variables. During training with labeled data, we treat these associated variables as if they had been intervened upon. Additionally, 600 extra samples are provided for testing purposes.
G.5 Semi-Supervised Learning
In this section, we delve into the influence of labeled data on the efficacy of DeepITE, with our findings summarized in Tables 7 Across all datasets under consideration, it is clear that the incorporation of labeled data yields a substantial enhancement in DeepITE's performance, with improvements in Recall @ 1 ranging between 4% to 20%, depending most pronounced gains are observed when the initial 5% to 10% of labeled data are integrated, with the rate of improvement tapering off beyond this point. This suggests that even a modest quantity of labeled data can lead to significant performance boosts, a fact that bears particular relevance in practical scenarios where acquiring limited amount of labeled data is typically feasible and can offer considerable benefits to DeepITE, Conversely, the other baseline methods in our study do not possess the capability leverage such labeling information to their advantage.
the dataset. Notably, the
G.6 Ablation Study
The ablation study of DeepITE, in comparison to VACA and DAG-GNN, is presented in Table 9 In Section (3.2 we have delved into the relationships between DeepITE, DAG-GNN, and VACA. We highlighted the limitations of DAG-GNN's inference model and VACA's generative model and illustrated how DeepITE overcomes these shortcomings. DAG-GNN utilizes an inference model represented as f4((I- AT Sa (x)), which has constraints can only gather messages from node's 's parents. On the other hand, DeepITE's inference model is crafted to aggregate messages from all nodes within the Markov blanket of given node, ensuring more flexible inference model tailored
hhtps:/wwww.iiiss......oooooeeee
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Tahle 9: Ablation study
MMD(Obs)
MMD(Int)
SSE
VACA
3,90 - 0.17
59.3 5.3
3742.46 1: 495.35
DAG-GNN
4.47 = 0.26
67.80 4 7.14
4525.58 + 539.04
Graph-50
DeepITE (VACA Decoder)
1,93 = 0.38
10.78 + 2.99
2534.70 + 105.22
DeepITE (DAG-GNN Encoder)
2.58 + 0.40
32.80 + 7.89
3177.97 + 189.61
DeepITE
0.12 = 0.075
0.76 = 0.051
415.66 + 26.92
VACA
4,58 + 0.26
70.89 + 4.14
4333.90 + 584,71
DAG-GNN
5.41 +: 0.30
89.71 + 6.33
5410.52 + 656.23
Graph-100
DeeplTE (VACA Decoder)
2.14 +: 0.15
14.7 1,53
2967 47 151.69
DeepITF: (DAG-GNN Encoder)
3.13 + 0.29
73.92 + 5.96
3651.14 245.47
DeepITE
0.19 = 0.073
0.96 0.066
490.06 37.15
VACA
5.36 + 0.54
116.30 + 2.59
6846.90
795.12
DAG-GNN
7.39 0.46
154.96 3.63
7511.26
916.93
Graph-500
DeepITE (VACA Decoder)
3.48 + 0.16
31.89 + 5.08
4391.59
340.15
DeepITE (DAG-GNN Encoder)
4.61 = 0.22
48.60 + 8.20
5316.33 454.54
DeepITE
1.08 0.045
5.16 + 0.65
731.17 + 75.32
for ITE tasks. VACA employs generative model with a minimum requirement of MPNN layers, where d represents the graph diameter. This limitation restricts the propagation distance of information within the graph, hindering its performance in estimating distributions over large graphs. DeepITE distinguishes itself by employing the generative model, specified in Eq. 7 thereby overcomes the limitations imposed by VACA's dependence on graph diameter.
To demonstrate the superiority of DeepITE's generative and inference models, we devised two ablation designs by seperately modifying DeepITE's encoder layers to DAG-GNN's encoder and DeepITE's decoder layers to VACA's decoder. These settings were compared alongside VACA and DAG-GNN. Based on the synthetic data in Appendix G.2] we combined the observational and interventional data and fed them into the model along with the adjacency matrix A for observational data, which is exactly how ITE works. Since VACA and DAG-GNN do not directly output ITE results, we utilized Maximum Mean Discrepancy (MMD) and the standard deviation of the squared error (SSE) between the true and estimated values for our evalutaion, providing another dimension to gauge their effectiveness in estimating ITE. MMD calculated separately for observational and interventional data, even though this information was unknown to the models.
The results, as shown in Table revealed DeepITE outperforming the other models, validating the excellence of DeepITE's generative and inference models. Due its relatively inflexible model design, DAG-GNN struggles to effectively reconstruct the biases associated with intervention points. The constraint of minimal number decoder layers limits VACA's capability capturing long-range dependencies and interactions within the graph structure, leading to a significant decrease in its performance as the number of graph nodes increase. Although the ablation methods showed suboptimal performance, the flexibility introduced by the ITE indicator enabled them to outperform the origin VACA and DAG-GNN. This underscores the adaptability of our approach for ITE tasks despite its limitations in certain scenarios.
G.7 Case Study
To address the challenges of root cause analysis in complex systems with interconnected variables, we conducted a case study on the real-world dataset ICASSP-SPGC 2022, evaluating the performance of DeepITE in comparison to other methods. The groundtruth causal graph is provided in (55). Recall that we focus on the RCA problem and we aim to identify and present the root causes of the system to users. Note that while we have labels for the root causes in our testing data, we only possess observations for the observable variables represented the graph. Consequently, all methodologies employed can only localize observable variables intervention targets (IT's), rather than directly identifying the root causes. For instance, RootCause (a weak signal marginal areas) can influence feature 19, feature X, and feature Y. However, RootCause also affects feature X. As result, when
22

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Table 10: Case study three different samples (1758, 1760, and 1093) from the real-world dataset ICASSP-
SPGC 2022. ITE Top-k represents k identified features. rool causes are then inferred from these features. The ground truths for these samples are rootcause2, rootcause3, and rootcause2rrotcaume
SAMPLE 1758
SAMPLE 1760
SAMPLE 1093
Top-1
ROOT CAUSE
ITE Top-1
ROOT CAUSE
ITE Top-2
ROOT CAUSE
CauseInfer
FeatureY
Unspecified
FeatureY
Unspocifted
Featur2&&Featund
Unspocified
MicroHECL
FeatureX
Unspecified
FeatureY
Unspecified
FeatrcX&&Faturreee
RooiCause3
MicroRCA
FeatureY
Unspecified
FeatureY
Unspocified
Feaurex&Feature00
RootCause3
CausalRCA
Feature
Unspecified
FeatureX
Unspecified
Feature Y Y&Fetuuee
Unspecified
CIRCA
FeatureY
Unspecified
FeatureX
Unspecified
Featurex&FFeatredd
RonCCause
RCD
FeatureY
Unspecified
FeatureX
Unspecified
Fecture FFFettueX
Unspecified
Expeanna
FeatureX
Unspecifiod
Featurel
Unspecifted
FeatureX& Featurel
Unspecified
ASV
FeatureY
Unspecified
FeatureX
Unspocifled
Feature' Y &Feature
Unspocified
ShapleyFlow
FeatureY
Unspecified
Featurel7
Unspecified
Feature &FeatuneX
Unspecified
PWSHAP
Feature
Unspecified
FeatureX
Unspecified
Feature Feature2
Unspecified
DeepITE
Feature19
RoaCause2
Feature60
RootCause3
Featur600&Fcature))
RootCause2&& Rootaass
UT-IGSP
CITE
10*
Causelnfer
MicroHECL
MicroRCA
Execution Runtime (sec)
10a
CIRCA
RCD
CausaiRCA
TreeExplainer
10a
ASV
ShapleyFlow
PWSHAP
10a
DeepiTE
10
#Nodes
Figure Runtime Analysis.
a given method identifies either feature X as an IT, it becomes challenging to ascertain whether RootCause 2 is indeed the true root cause.
Interestingly, DeepITE demonstrates superior performance on this dataset by effectively identifying features exclusive to specific root cause. We evaluated the performance of all methods samples 1758, 1760, and 1093, and summarized the results in Table I0) For sample 1758, where the true root cause is RootCause 2, DeepITE identifies feature 19 the IT, thus it is evident that RootCause2 should be the root cause. contrast, other methods identify either feature or feature Y, making difficult pinpoint the true root cause. Similarly, for sample 1760. DeepITE identifies feature60 as the IT, which is also exclusive to the true root cause, RootCause 3. On the other hand, in the case of sample 1093, DeepITE selects both feature60 and feature 19, enabling to conclude that both RootCause 2 and RootCause 3 are relevant root causes, a finding that aligns with the ground truth.
H Limitations
One limitation of the DeepITE framework is its applicability restricted cases with fully observed causal graphs, presuming the absence of confounders. Real-world scenarios may involve confounding. where relationships between observed variables are influenced by latent variables. Addressing this challenge-hor to effectively handle confounding in the presence of unobserved factors represents a compelling avenue for future research. Additionally, DeepITE presupposes the availability of pre-specified graph structure. While causal discovery techniques can be applied to ascertain the graph
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configuration when it is not known, the joint pursuit of determining both the graph structure and the intervention targets simultaneously offers a tantalizing challenge for future exploration. Finally, proving the consistency and identifiability of DeepITT,, and more broadly in the application of VGAFs for ITT, remains an interesting avenue for future work. Notably, such theoretical guarantees have been established for VGAEs in the context of causal inference (both observational and interventional) [56].
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Reviews:
[{"review_id": "T7bIrDF3wP", "reviewer": "Reviewer_q8ww", "summary": "Given a causal graph, this paper describes an autoencoder-based approach specifically designed for the purpose of designing intervention targets. This algorithm aims to be data and computation-efficient by by-passing the task of having to recover the causal graph, and also by incorporating specific architectural designs for the auto-encoder.", "questions": "I have a few questions for the authors: \n\n1. What is the purpose of the noise variable \\zeta, and why is it modeled as a separate variable from the \\mathbf{u} variable? From my experience, the exogenous variables are simply independent noise terms that are provided as input to the functional causal model. \n\n2. How do the authors know the true intervention targets of the protein dataset? \n\n3. Have the authors considered the following baseline: given M interventional datasets and an observational dataset, label each point with label G corresponding to which interventional dataset it comes from. Then, train a sparse classification (feature selection) method such as sparse logistic regression to predict G from the features X when combining an interventional and an observational dataset. Then, the selected features will be the Markov Blanket of G. I was wondering what the author's thoughts are on this simple baseline.  \n\n4. In line 300, the authors mention: \"Once trained, the inference model of DeepITE becomes equipped to evaluate individual new samples\nagainst different causal graphs, directly deducing the intervention targets and thus circumventing\nthe necessity of retraining for each new scenario....\"; could the authors elaborate on this point? My understanding is that the model assumes a given causal graph.", "limitations": "From what I see, there is no discussion on this model's limitation in the text.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The main strengths of this paper are as follows: \n\n1. Empirical results: the results of this paper seem to be very convincing, and there is comparison to many related baselines. \n\n2. The method is concise and easy to implement/understand and relate to prior work.", "weaknesses": "The weaknesses of this paper are as follows: \n\n1. The code is not available -- the main contributions of this work are experiments and empirical evaluation, which are quite strong. In order to truly assess this work, code availability, while not the only factor, is very important. \n\n2. Lack of clarity: a) There is lack of clarity in terms of the datasets and evaluations (see questions below); b) there is lack of clarity on the competing methods and how this method fits within the general framework. For example, it would be great if RCA, and the main ITE baselines can be formally described in mathematical terms through a simple example (either in the main text or the appendix). \n\n3. Evaluation: the authors seem to not discuss a very trivial and intuitive baseline regarding prediction methods with sparsity such as Lasso (please see questions below).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "_Q1 code availability_\n\nWe are committed to open-sourcing the code upon acceptance of the paper. \n\n_Q2 lack of clarity_\n\n1. Datasets and Evaluations: We have provided descriptions of the datasets used, including their sources, preprocessing steps, and evaluation metrics in Appendix G (please refer to Appendix G.2-G.5)\n2. Competing Methods: In Appendix B (pages 13–14), we have provided a review of the existing methods in XAI and RCA. **This section includes a concise explanation of how each method operates**. While we acknowledge that mathematical formulations can be rigorous, we believe that explaining these methods in plain language enhances clarity and comprehension. Additionally, **we have referred readers to Appendix B within the experiment section (see Line 316 on Page 7) for easy access.**\n\n_Q3 observation noise_\n\nDeepITE includes the observation noise variable $\\epsilon$ that reflects uncertainty not present in the true SCM. In the true SCM, observed variables are deterministic functions of their exogenous variables and parent nodes via the structural equations (SEs). Since DeepITE does not have access to the true SEs or the distribution of the exogenous variables, it assumes the SEs can be approximated as $Dec(\\mathbf{u}, \\gamma, A) = f_2((I- A_\\mathcal{I}^T)^{-1}f_1(\\mathbf{u}))$ (Eq. (7)), with learnable $f_1$ and $f_2$. Thus, $\\epsilon$ represents the uncertainty in the estimated observational distribution resulting from this approximation. Note that $\\epsilon$ has also been used in VACA [21] for the same sake. **We have clarified this point on Lines 222-224 in Section 5.1.**\n\n_Q4 protein dataset_\n\nWe clarify that the true intervention targets of the protein dataset are known because we utilize the dataset from IGSP [R1], which provides this information in Appendix E in their paper. Specifically, this datasets contain measurements of proteins and phospolipids under different interventional environments. In each environment, signaling nodes are inhibited or activated. Hence, **these sites form intervention targets.** This dataset has been previously employed in [3,6,10] for ITE. We will explicitly mention this in the dataset introduction in Appendix G.4.\n\n_Q5 Lasso_\n\nWe respectfully believe that Lasso does not align with the scope of ITE as outlined Definition 1 on Page 4. First, **Lasso cannot incorporate the causal graph structure available in ITE**. The prediction G can be an arbitrary variable in the graph and there is no clear guideline on how to choose G given the observed data. Moreover, **Lasso is designed to identify correlations rather than causal relationships, and so it is typically used for undirected graphical models [R2]**. Consequently, Lasso may select all variables correlated with G, rather than correctly identifying the intervention targets that directly impact G.\n\nActually, Lasso has been used for RCA in [8], but only for **bipartite** directed graphs where causal and effect relationships between features and predictions have been provided. In contrast, our approach is designed to handle more complex causal graph structures where the predictions are unknown.\n\n_Q6 given causal graph_\n\nYou are correct that DeepITE assumes a given causal graph (see Defintion 1 on Page 4). However, once trained, it can evaluate new samples on new causal graphs without needing to retrain. By directly inputting the sample and the new graph structure into the inference network, we can derive the intervention targets. This stands in contrast to classical ITE methods, which require retraining for each new causal graph and each new set of intervention targets, even if the graph remains unchanged.\n\n_Q7 limitations_\n\nWe have included a discussion of the limitations in Appendix H (Page 21).\n\n[R1] Wang et al, Permutation-based Causal Inference Algorithms with Interventions, NIPS 2017.\n\n[R2] Meinshausen et al, High-dimensional Graphs and Variable Selection with the Lasso, Ann. Statist., 2006.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "reMy3lyoLR", "reviewer": "Reviewer_GccQ", "summary": "This paper proposes a deep learning approach for Intervention Target Estimation (ITE) which is an important problem in causal discovery and inference. The authors argue that traditional methods in this area can only independently process each instance and is computationally inefficient. To address these limitations, the authors propose a variational graph auto-encoder whose key components involve an encoder and a decoder that are specially designed to accommodate the properties of causal factorization/intervention. The authors also discuss how to instantiate the encoder and decoder with modern GNNs and use the variational inference for deriving the objective. Experiments on synthetic datasets and two real-world datasets verify the effectiveness of the model against several state-of-the-art baselines.", "questions": "1. What precisely the semi-supervised learning is conducted using the model? How the labeled data is incorporated into the ELBO?\n\n2. Can the authors provide more explanations why Recall is used as the only metric for evaluation?\n\n3. Given the studied problem, maybe more qualitative analysis or case study are expected to further verify the model.\n\nMinor: \n\n1. The authors argue that GAT enables inductiveness and consider this as the advantage of GAT over other GNNs. This arguement seems problematic, since in the context of the proposed framework, the GAT can be in principle replaced by other off-the-shelf GNNs, such as basically GCN and SGC. Maybe more explanations or ablation studies are needed for jusitification on this design choice.\n\n2. Some typos for reference: \n\nline 106: \"but this too suffers from scalability issues with large graphs\"\n\nline 109: \"zeroes in on ITE\"", "limitations": "The limitations are properly discussed in the appendix.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The paper studies an important problem whose significance is embodied by extensive applications in various domains.\n\nThe proposed method seems interesting and technically sound. The proposed components are properly justified with theoretical analysis and explanations against potential alternatives. Also, comparison with related models is sufficiently conducted.\n\nThe experiments are solid and comprehensive. The improvements achieved over the state-of-the-arts look promising.", "weaknesses": "The model section is not entirely clear, and some parts need further explanation.\n\nThe technical novelty is somehow weakened given that the direct usage of DAG-GNN in Sec 5.1.\n\nThe experiments lack qualitative analysis or case study to verify the model.\n\nThe presentation can be improved, especially the descriptions about prior art and the motivation in Sec. 1 (the third and fourth para are too long and involved).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your positive evaluation and encouraging feedback on our paper. We deeply appreciate your constructive comments and valuable suggestions. \n\n_Q1 direct use of DAG-GNN_\n\nDeepITE does not directly use DAG-GNN. **We have explcitly discussed the relation between DeepITE and DAG-GNN on Lines 260-270 on Pages 6-7.** In summary, the key distinction lies in their objectives: DAG-GNN is designed for causal discovery (learning the DAG structure), whereas DeepITE focuses on ITE. Although both models share some similarities as variants of VGAEs, their inference models and latent spaces differ significantly. Furthermore, **our ablation study in Appendix G.6 demonstrates the superiority of DeepITE over DAG-GNN for ITE tasks.**\n\n_Q2 case study_\n\nThanks for pointing it out! We have included a case study as follows:\n> We first present the causal graph in Figure R2. Recall that we focus on the RCA problem and we aim to identify and present the root causes of the system to users. Note that while we have labels for the root causes in our testing data, we only possess observations for the observable variables represented in the graph. Consequently, all methodologies employed can only localize observable variables as intervention targets (ITs), rather than directly identifying the root causes. For instance, RootCause 2 (a weak signal in marginal areas) can influence feature19, feature X, and feature Y. However, RootCause 3 also affects feature X. As a result, when a given method identifies either feature X as an IT, it becomes challenging to ascertain whether RootCause 2 is indeed the true root cause.\n\n> Interestingly, **DeepITE model demonstrates superior performance on this dataset by effectively identifying features exclusive to a specific root cause.** We evaluated the performance of all methods on samples 1758, 1760, and 1093, and summarized the results in Table R2. For sample 1758, where the true root cause is RootCause 2, DeepITE identifies feature19 as the IT, thus it is evident that RootCause2 should be the root cause. In contrast, other methods identify either feature X or feature Y, making it difficult to pinpoint the true root cause. Similarly, for sample 1760, DeepITE identifies feature60 as the IT, which is also exclusive to the true root cause, RootCause 3. On the other hand, in the case of sample 1093, DeepITE selects both feature60 and feature19, enabling us to conclude that both RootCause 2 and RootCause 3 are relevant root causes, a finding that aligns with the ground truth.\n\n_Q3 presentation_\n\nTo address your concern, we will break up the third and fourth paragraphs into two shorter paragraphs each (e.g., the third paragraph can be divided at Line 46). Additionally, we will provide an introduction to less well-known priors, such as Jeffrey's prior, in the appendix, which will further enrich the reader's understanding of the priors.**\n\n_Q4 semi-supervised learning & labeled data_\n\nAs detailed in Lines 283-309 in Section 5.3 (Page 7), the labeled data can be utilized to train the inference network, enabling it to more accurately identify intervention targets. In particular, the term $q(\\gamma_i|\\mathbf{x}, A)$ is replaced by the ground truth $\\gamma_i$ when computing the ELBO (Eq. (14)), and an additional term is introduced to maximize the log-likelihood $\\log q(\\gamma_i^*|\\mathbf{x}, A)$.\n\n_Q5 why use Recall_\n\nAs defined in 326-328 on Page 8, Recall@k measures th proportion of true intervention targets (ITs) that are successfully captured within the top k ranked candidates proposed by each method. When k = 1, our goal is to pinpoint the intervention targets based on the highest-ranked candidate. We prioritize Recall@k because, in practice, false positives can be eliminated through further analysis, while false negatives are irrecoverable as they get lost among the numerous true negatives. This metric is widely adopted in the literature [4, 9, 29]. **We will clarify this point in the revised paper.**\n\nFurthermore, **we have incorporated additional metrics in our evaluations**, such as Root.Acc and Score in the ICASSP experiments (see Table 7), and MMD/MSE in the ablation study (see Table 8).\n\n_Q6 why use GAT_\n\nWe acknowledge that any inductive spatial GNN can be used as the inference network in DeepITE, and **will mention this point in our paper**. However, **we choose GAT since it is more flexible compared to GCN and SGC** [R3]. This flexibility stems from GAT's ability to dynamically weigh the importance of different nodes, thus allowing the variational distribution given by the inference network better approximate the exact posterior distribution. **This advantage has been demonstrated in Appendix G.6 (Page 20), where we replace the GAT encoder with the encoder of DAG-GNN, a type of GCN [15], and show the benefits of using GAT.**\n\n_Q7 typos_\n\nWe will fix the typos accordingly.\n\n[R3] Veličković, et al, Graph Attention Networks, ICLR 2018.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fNTjN0BuVO", "reviewer": "Reviewer_bhQC", "summary": "The paper presents DeepITE, a novel deep learning framework designed for Intervention Target Estimation (ITE) in complex systems. DeepITE addresses these issues by employing a variational graph autoencoder (VGAE) that can learn from both unlabeled and labeled data across various intervention targets and causal graphs. The framework is capable of self-supervised and semi-supervised learning, allowing it to identify intervention targets without the need for retraining with new instances. It demonstrates improved performance in Recall@k metrics and faster inference times, especially for large graphs.", "questions": "1. **Identifiability**: Does DeepITE have decidability, and can it guarantee that the optimal set of intervention variables can always be recovered from the data?\n2. **Sample size**: Since this method is based on deep networks, are there any specific requirements for the amount of data samples?\n3. **Robustness to random initialization**: according to algorithm 1,  this method need to be initialized by some random parameters. Does it robustness to the random initialization?", "limitations": "NA", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. **Originality**: DeepITE addresses a novel problem formulation in the context of causal analysis, focusing on estimating intervention targets in a manner that is not addressed by existing methods. Additionally, this method overcomes limitations of prior work by enabling collaborative learning across different instances and effectively incorporating labeled data, which was underutilized in previous approaches.\n2. **Empirical Validation**: Extensive experiments are conducted, comparing DeepITE against 13 baseline methods on both synthetic datasets and real-world dataset, demonstrating its superiority in Recall@k metrics and inference time, particularly for large graphs.\n3. **Presentation**: This paper clearly introduces the ITE problem and existing solutions, and elucidates the relationship between the proposed method and existing works (DAG-GNN and VACA), emphasizing the flexibility and effectiveness of DeepITE in handling graphs of different sizes and structures.", "weaknesses": "While the paper presents a significant contribution with the introduction of DeepITE, there are several areas where the work could be improved towards its stated goals:\n1. **Theoretical Depth**: The paper could benefit from a more thorough theoretical analysis, particularly in understanding the limitations of the VGAE approach compared to traditional causal inference methods. For instance, a deeper discussion on the assumptions made by DeepITE and how they compare to assumptions in established causal frameworks would be valuable.\n2. **Scalability Analysis**: While the paper mentions the efficiency of DeepITE, a more detailed analysis of its scalability with respect to the size of the graph and the number of interventions would be beneficial. This could include profiling the computational complexity and memory usage for very large graphs.\n3. **Intervention Types**: DeepITE is designed to handle interventions, but the paper could provide more details on the types of interventions it can effectively estimate. For example, it would be useful to know how the model performs with different mixes of hard and soft interventions.\n4. **Confounding Factors**: The paper assumes the absence of confounders, which may not hold in real-world scenarios. Future work could explore extensions of DeepITE to handle potential confounding, possibly through the integration of domain knowledge or advanced causal inference techniques.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "_Q1 theory_\n\nWe acknowledge the importance of theoretical analysis. However, due to the time limit, **we instead provide a comparative analysis of the assumptions inherent in various methods in Table R1 of the PDF attached to our global response.** This table highlights that the primary advantages of DeepITE are its scalability and the flexible amortization during inference, without the need for retraining on new graphs and intervention targets (ITs). However, it struggles to address confounders, and it requires the graph structure as an input, despite its capacity to learn structural equations. **These two limitations have been mentioned in Appendix H (Page 20).**\n\n_Q2 scalability_\n\nThank you for your insightful feedback! We have provided the performance of DeepITE as a function of the graph size and the number of interventions respectively in Figs R1(a-b) in the attached PDF. Our findings indicate that while performance in terms of Recall@1 declines as the graph size increases, Recall@5 remains stable (above 0.9), suggesting that the **true ITs consistently appear among the top 5 candidates, even for graphs with 1000 nodes—a size that is already considered quite large for causal analysis**. Moreover, as the number of interventions rises, DeepITE's performance decreases gradually; however, **even with 5 interventions, the Recall@k remains at 0.825**.\n\n_Q3 intervention types_\n\nThanks for your constructive suggestion! We have examined DeepITE's performance with various proportions of hard and soft interventions. Soft interventions are modeled by replacing the linear structural equations related to intervention targets with quadratic ones. The results, presented in Figure R1(c) of the attached PDF, show that **DeepITE is robust to different mixtures of hard and soft interventions, demonstrating its capability to handle both types effectively.** In addition, as detailed in Appendix G.3 (Page 18), we conducted experiments based on classical ITE settings with soft interventions. We will mention \"soft interventions\" explicitly in that section. The results, shown in Table 5 in our paper, reinforce **the superiority of DeepITE over other baseline models in dealing with soft interventions.**\n\n_Q4 confounders_\n\n**We acknowledge the importance of addressing confounders as a critical future work in Appendix H (Page 21)**. In line with other ITE works, where algorithms are initially developed for scenarios without confounders [3] before being extended to handle confounders [6], we also plan to extend DeepITE for confounders in future work.\n\n_Q5 identifiablity_\n\nIn our paper, we primarily demonstrate that the decoder, as defined in Eq. (5), can address interventional queries by modifying the adjacency matrix $A$ through graph surgery (see Propositions 1-3), since **this mechanism lays the foundation for deriving DeepITE for ITE**. Whether the optimal set of ITs can be detected relies on the properties of the amortized variational inference framework we use. \n\nOn the other hand, we notice that **the identifiability of VGAE for causal inference (the inverse process of ITE, see Lines 158-162) has been proven in [R4]**. Due to the time limit, we are unable to prove the identifiability of DeepITE for now, but **will highlight this limitation in Appendix H, identifying it as a promising area for future research:**\n> Finally, proving the consistency and identifiability of DeepITE, and more broadly in the application of VGAEs for ITE, remains an interesting avenue for future work. Notably, such theoretical guarantees have been established for VGAEs in the context of causal inference (both observational and interventional) in [R4].\n\n\n_Q6 sample size_\n\nThanks for pointing it out! We have depicted the performance of DeepITE as a function of the sample size in Figure R1(d) in the attached PDF. Here we choose 10 graphs for training and change the sample size from 25 to 1000 for each graph. Our findings indicate that **DeepITE is generally robust to variations in sample size, though a larger sample size can enhance its performance**. In particular, on graphs with 50 nodes, DeepITE achieves a Recall@1 of 0.812 and a Recall@5 of 0.984 even with just 50 samples for each graph (500 samples in total). This success may stem from the collaborative learning approach in DeepITE and the relatively few parameters in the GNN-based encoder and decoder. In contrast, traditional ITE methods [3,6,10] typically require thousands of samples for a single graph and intervention set to perform well. However, it is also important to note that **the performance of DeepITE declines rapidly when the sample size is extremely small (e.g., 25 samples per graph), which aligns with our expectations**.\n\n_Q7 random initialization_\n\nDeepITE is robust to random initialization. **Since each node in a graph is initialized randomly and all graphs are trained collaboratively**, the method can converge to optimal solutions regardless of the initial parameters. **We will mention this point in our revised paper.**\n\n[R4] Zečević et al. Relating GNN to structural causal models, 2021.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wtChQKUKBu", "reviewer": "Reviewer_HUbk", "summary": "The paper introduces a deep learning framework for identifying intervention targets in causal systems by amortizing the problem, although assuming that the causal graph is known. The framework, called DeepITE, employs a variational graph autoencoder (VGAE) that learns from both labeled and unlabeled data across different intervention targets and causal graphs. The model can quickly identify intervention targets in new samples without the need for retraining, addressing the inefficiencies of current methods which require frequent re-estimations with minor data changes. DeepITE is validated through comprehensive testing against 13 baseline methods, showing superior performance.", "questions": "- Can you clarify whether the framework assumes the causal graph is known a priori, as this seems to differ from approaches like Yang et al. [7]? Would it be possible to include a table summarizing key assumptions of different RCA and ITE efforts for clearer comparative context?\n- Can you substantiate the claim that the model can handle soft interventions, given the complexities associated with different types of interventions and the potential for model misspecification?\n- Is there any consistency theory or additional theoretical backing for the proposed method, especially regarding its generative model and causal semantics?\n- How do you justify the claim about the model's ability to amortize across different settings, particularly given the mixed performance of DeepITE in various configurations as noted in your results?\n- Could you discuss under what conditions the model is expected to perform well, which components significantly contribute to its empirical success, and any limitations in its current design?", "limitations": "N/A", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Originality\nThe proposed variational graph autoencoder (VGAE) framework tailored for the task of Intervention Target Estimation (ITE) is novel, to the best of my knowledge. The authors propose to repurpose some of the VGAE framework for GNN-DAG for the purpose of learning the intervention target, so the approach is not entirely novel, but their application of it to the ITE problem definitely is.\n\nQuality\nThe methodology is sound and well explained. As far as I can tell, the experiments are well-designed. The method is validated against a variety of datasets and baseline methods. The empirical results convincingly support the superiority of DeepITE over existing models, particularly in terms of scalability and efficiency in handling large and complex graphs. \n\nClarity\nThe paper is well-written.\n\nSignificance\nThis work is significant, it may be important for root-cause analysis in systems where the causal graph is known (which is not often the case).", "weaknesses": "1. The authors should make it explicit from the introduction that their framework assumes that the graph is known (but not the full SCM). As far as I understand, this is not the case in Yang et al. [7]. I believe the manuscript would be clearer if it had a table summarizing the different efforts for RCA and ITE, explaining for each paper what they assume (wrt knowledge of causal model, nature of intervention, max number of nodes targeted, and any other relevant assumptions), so that a reader may situate the work more carefully.\n2. The claim that the model can handle soft interventions seems uninstantiated. I quote \"A probability of gamma_i = 0 being one indicates a hard intervention, whereas any other value suggests a soft intervention\". Given that there are many different layers of complexity at play here, I do not believe this is a reasonable claim. For example, this is a clear instance of model misspecification, in which I do not expect there there would be consistency. For certain type of soft interventions, it is plausible that the proposed model (based on hard interventions) would erroneously predict the set of intervened nodes. Additionally, the model's inference strategy uses variational approximations, and differentiable relaxation of discrete distributions, which makes it very hard to assess whether \"probability of gamma_i = 0 [equals] one\".\n3. I wish to have seen some form of consistency theory for the proposed method. So far it has little to no theoretical backing, even though I understand that the authors have explained well why the generative model has a nice causal semantic.\n4. A main advantage of the method is to amortize across different settings, but I am not sure this claim is perfectly justified. For example, DeepITE mix performs worse than the sep variant and it performs worse than CI-RCA in table 1 (unlike written in the text!). Additionally, the authors didn't quite explain how many graphs were put together, and I didn't see in the main text the existence of an ablation study to show that with more graphs, the method performs better. I wish to have seen a assessment of the amortization to justify that it actually works.  \n5. It would be great if the authors could explain when they expect the model to perform well, or not, and which components are helpful for its empirical success.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "_Q1 summarizing table_\n\nWe agree that LIT does not require a given graph and will mention DeepITE requires a given graph explicitly in the introduction. In response to your suggestion, **we have provided a comparative analysis of the assumptions inherent in various methods in Table R1 of the PDF attached to our global response.** This table highlights that the primary advantages of DeepITE are its scalability and the flexible amortization during inference, allowing for adaptation without the need for retraining on new graphs and intervention targets. It also has limitations: it struggles to address confounders, and requires the graph structure as an input, despite its capacity to learn structural equations. **These two limitations have been mentioned in Appendix H (Page 20).**\n\n_Q2 soft interventions_\n\nThank you for your valuable feedback! We have examined DeepITE's performance with various proportions of hard and soft interventions. Soft interventions are modeled by replacing the linear structural equations related to intervention targets with quadratic ones. The results, presented in Figure R1(c) of the attached PDF, show that **DeepITE is robust to different mixtures of hard and soft interventions, demonstrating its capability to handle both types effectively.** In addition, as detailed in Appendix G.3 on Page 18, we conduct further experiments based on classical ITE settings that also involve soft interventions. We will mention \"soft interventions\" explicitly in that section. The results, showcased in Table 5 in our paper, reinforce **the superiority of DeepITE over other baseline models in these scenarios, further confirming its effectiveness in dealing with soft interventions.**\n\n_Q3 consistency_\n\nIn our paper, we primarily demonstrate that the decoder, as defined in Eq (5), can address interventional queries by modifying the adjacency matrix $A$ through graph surgery (see Propositions 1-3), since **this mechanism lays the foundation for deriving DeepITE for ITE**. Whether the estimated set of intervention targets is consistent with the ground truth relies on the properties of the amortized variational inference framework we use. \n\nOn the other hand, we notice that **the consistency of VGAE for causal inference (the inverse process of ITE, see Lines 158-162) has been proven in [R4]**. Due to the time limit, we are unable to prove the consistency of DeepITE at this moment, but **will highlight this limitation in Appendix H (Page 21), identifying it as a promising area for future research:**\n> Finally, proving the consistency and identifiability of DeepITE, and more broadly in the application of VGAEs for ITE, remains an interesting avenue for future work. Notably, such theoretical guarantees have been established for VGAEs in the context of causal inference (both observational and interventional) in [R4].\n\n\n_Q4 amortization_\n\nThank for your constructive suggestion! We have investigated the performance of DeepITE as more graphs with different sizes are trained together. Specifically, we begin with 100 graphs, each containing 50 nodes and 1000 samples, and assess the model's performance on testing graphs containing 50 nodes. We subsequently introduce additional groups of graphs: 50 graphs with 100 nodes, followed by another 50 graphs also with 100 nodes, and finally 50 graphs with 500 nodes, followed by another 50 graphs also with 500 nodes. \n\nAs detailed in Figure R1(e), our findings indicate a gradual degradation in the performance of DeepITE (mix) as we incorporate more graphs of varying sizes, attributed to the amortization error. However, **this reduction in performance is minimal.**\n\nMoreover, the results in Table 1 of our original paper show that while DeepITE (mix) performs slightly worse than DeepITE (sep), it even outperforms DeepITE (sep) training exclusively on 100-node graphs in terms of Recall@1. Based on this evidence, **we maintain that the amortization process across graphs does not significantly hinder the performance of DeepITE.**\n\n_Q5 performance boundary & components contribution_\n\nThank you for your constructive feedback! We have conducted additional ablation studies to investigate the performance of DeepITE under various conditions, specifically focusing on graph size, the number of interventions, and sample size. The findings are illustrated in Figure R1 in the attached PDF. Our results indicate that the **performance of DeepITE gradually declines as graph size and the number of interventions increase, and as sample size decreases, which aligns with our expectations.**\n\nTo evaluate the contributions of different components within DeepITE, we performed a detailed ablation study, the results of which can be found in Appendix G.6 (Page 20). Our analysis revealed that the **specific designs of both the encoder and decoder play critical roles in the model's empirical success.**\n\nLastly, **we have outlined the limitations of DeepITE in Appendix H (Page 20).**\n\n[R4] Zečević et al. Relating GNN to structural causal models, 2021.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
**Global response to all reviewers**:

We sincerely thank all the reviewers for their valuable suggestions. We are delighted by the unanimous recognition of our work and appreciate the reviewers' positive feedback on the carefully designed network architecture and extensive experiments in DeepITE.

We have thoroughly reviewed each of the reviewers' questions and suggestions, and we are grateful for their patience and diligence. In response, we have **conducted additional experiments** to evaluate the performance of DeepITE as a function of (a) graph sizes, (b) the number of interventions, (c) the mixture proportion of soft and hard interventions, (d) sample size for each graph, and (e) the number of mixed graphs, added new analyses to address any lingering questions, and emphasized the significance of our work. We have also **included a summary table** that highlights the characteristics of all compared methods and **a detailed case study** to further illustrate the practical application and effectiveness of our approach.

In the following rebuttal, we address each reviewer's comments individually. The reviewer's comments are presented in italics, followed by our response. Quotations from the revised paper are included in markdown quotation mode. Unless otherwise specified, all references to pages, equations, sections, and bibliographic citations relate to the revised paper. Additionally, figures, tables, and citations prefixed with "R" (e.g., [R1]) are newly added citations in this rebuttal. All newly added images and a table are enclosed within a separate single-page PDF attached to this global response. We will incorporate the suggested revisions into the final camera-ready version to enhance the clarity and persuasiveness of our paper.

Once again, we would like to express our gratitude to the reviewers for their insightful feedback, which has helped us identify areas for improvement and refine our work. We welcome any further insights or concerns that would contribute to enhancing the paper according to the reviewers' perspectives.

Meta-review: I recommend the work "DeepITE" for acceptance based on its significant contributions and overall positive feedback. The work introduces a novel framework for intervention target estimation using a variational graph autoencoder (VGAE) and is empirically validated to outperform baseline methods, demonstrating superior scalability and efficiency. The paper is well-written and connects the new model to existing methods. 

The score of this work has benefited a lot from the discussion between authors and reviewers. Many points have been successfully and extensively clarified and addressed by the authors. 

However, there are some weaknesses that need to be addressed before the final camera-ready version. These include , clarifying assumptions related to the causal graph and intervention types, improving the theoretical analysis concerning scalability and confounder handling and providing accessible implementation code to enhance reproducibility. Addressing these points will strengthen the paper and ensure its contribution is fully validated.
Decision: Accept (poster)