ALM-MTA: Front-Door Causal Multi-Touch Attribution Method for Creator-Ecosystem Optimization
Authors: Yuguang Liu
Luyao Xia
Hu Liu
yanzhangxi
Jian Liang
Han Li
Kun Gai
Venue: ICLR 2026 Poster (2026)
Keywords: causal reasoning
multi‑touch-attribution
recommendation system
Primary area: causal reasoning

Abstract: Consumption‑Drives‑Production (CDP) on social platforms aims to deliver interpretable incentive signals for creator‑ecosystem building and resource utilization improvement, which strongly relies on attributions. In large-scale and complex recommendation system, the absence of accurate labels together with unobserved confounding renders backdoor adjustments alone insufficient for reliable attribution. To address these problems, we propose Adversarial Learning Mediator based Multi‑Touch-Attribution (ALM-MTA), an extensible causal framework that leverages front-door identification with an adversarially learned mediator: a proxy trained to distillate outcome information to strengthen causal pathway from treatment to outcome and eliminate shortcut leakage. Then, we introduce contrastive learning that conditions front door marginalization on high match consumption upload pairs for ensuring positivity in large treatment spaces. To assess causality from non‑RCT logs, we also incorporate a non‑personalized bucketed protocol, estimating grouped uplift and computing AUUC over treatment clusters. Finally, we evaluate ALM-MTA performance using a real-world recommendation system with 400 million DAU (daily active users) and 30 billion samples. ALM-MTA has increased DAU with 0.04% and 0.6% of the daily active creators, with unit exposure efficiency increased by 670%. On causal utility, ALM-MTA achieves higher grouped AUUC than the SOTA in every propensity bucket, with a maximum gain of 0.070. In terms of accuracy, ALM-MTA improves upload AUC by 40% compared to SOTA. These results demonstrate that front -door deconfounding with adversarial mediator learning provides accurate, personalized and operationally efficient attribution for creator ecosystem optimization.

ALM-MTA..RONTT-DOR CAUSAL MULCH-TOUCH ATTRI- BUTION METHOD FOR CREATOR-ECOSYSTEM OPTIMIZA- TION
Yuguang Liu
Beijing Dajia Internet Information Technology Co., Ltd. Liuyuguang@kinn sho.. com
Luyao Xia
The University of Auckland. luyao. xia auckland.acc.mmcm ac.nz
Hu Liu
Beijing Dajia Internet Information Technology Co., Ltd. hooglecrystas atssstllllaa al@126..l....11..11 com
Zhangxi Yan
Beijing Dajia Internet Information Technology Co., Ltd. yanzhangx @kua iso... com
Jian Liang
Beijing Dajia Internet Information Technology Co., Ltd.
Mlangjiant 10kuaaskuuu.u...aaaaaaaaaaaaauuu com
Han Li
Beijing Dajia Internet Information Technology Co., Ltd. Lihan080kual com
Kun Gai
Beijing Dajia Internet Information Technology Co., Ltd. Srrruee@@kuiishahe com
ABSTRACT
Consumption Drives Production (CDP) social platforms aims to deliver interpretable incentive signals for creator ecosystem building and resource utilization improvement, which strongly relies attributions. In large-scale and complex recommendation sys- tem, the absence of accurate labels together with unobserved confounding renders back- door adjustments alone insufficient for reliable attribution. To address these problems, we propose Adversarial Learning Mediator based Multi Touch Attribution (ALM-MTA), an extensible causal framework that leverages front-door identification with an adversarially learned mediator: proxy trained to distillate outcome information to strengthen causal pathway from treatment outcome and eliminate shortcut leakage. Then, we introduce contrastive learning that conditions front door marginalization on high match consumption upload pairs for ensuring positivity large treatment spaces. To assess causality from non RCT logs, also incorporate non personalized bucketed protocol. estimating grouped uplift and computing AUUC over treatment clusters. Finally, we evaluate ALM-MTA per- formance using real-world recommendation system with 400 million DAU (daily active users) and 30 billion samples. ALM-MTA has increased DAU with 0.04% and 0.6% of the daily active creators, with unit exposure efficiency increased by 670%. On causal utility, ALM-MTA achieves higher grouped AUUC than the SOTA in every propensity bucket,
Equal contribution

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with a maximum gain of 0.070. In terms of accuracy, ALM-MTA improves upload AUC by 40% compared to SOTA. These results demonstrate that front-door deconfounding with adversarial mediator learning provides accurate, personalized and operationally efficient attribution for creator ecosystem optimization.
INTRODUCTION
platform-mediatee creator economies, content platforms have exhibited stable 'consumption-drives production' pattern: users are more likely to transition into creation (i.e., upload new content) after con- suming sufficiently 'inspiring' content Yao et al. (2024): ( Toool (2023). Accordingly, understanding and quantifying the drivers of user-generated content enables platforms present content that inspires creation, thereby cultivating a vibrant creator ecosystem and optimizing resource allocation ["Toolle (2023); y [hhr- gava (002)). Within recommender systems, the task is naturally formalized as nouti--ouch attribution
(MTA) problem, in which multiple prior touchpoints in a user's 's consumption sequence may jointly lead to the eventual conversion (an upload) [Shender et al. (2020); Yao et al. (2022). Accurate causal attribution is not only essential for interpretability, but it also directly impacts on incentive design, cold-start handling, and re-ranking optimization Liu al. (2025a). However, large-scale, complex recommender systems, multi-touch attribution is jointly constrained by (i) the absence of explicit ground truth, (ii) pervasive multi- space, rendering reliable and scalable causal attribution a central challenge for system design and rreator-ecosyster governance governance/Luo et al.) (2024).
source confounding, and (iii) the large-scale
Within these constraints, industrial practice typically bifurcates attribution into strict rule based methods and semantic/path similarity methods. The former delivers high precision yet very limited coverage, whereas the latter broadens coverage but lacks causal identifiability and often conflates correlation with cause Gao et al. (2024). Both methods fail to estimate the counterfactual effect the upload outcome of removing specific video within the video consumption sequence, while preserving the rest of the trajectory (see Fig D. At the individual level, estimating a personalized causal effect requires quantifying, within user's 's sequen- tial consumption context, each touchpoint's 's marginal uplift in that user's 's upload probability producing an intra-individual ranking by causal strength WWang et al.] (2025); [Tang (2024. Since rule- semantic-based methods typically rely on cohort-level averaging and similarity scores, ignoring heterogeneous treatment effects (HTE), sequence effects, and interactions among touchpoints, they are ultimately difficult to provide personalized effect estimates at the user level Lee et al. (2025); Zhan et al. (2024). Consequently, conven- tional methods cannot simultaneously achieve precision, coverage, and personalization.
To address these challenges, we present novel counterfactual causal multi-touch at- tribution framework that defines a touch- point's uplift as the drop in predicted up- load probability when that touchpoint is deleted from the video consumption se- quence. Given unobserved confounding in complex systems, backdoor adjustment alone cannot ensure causal identifiability. We therefore invoke front-door identifica- tion by introducing latent mediator that transmits the causal pathway from con- sumption to upload. This mediator can be aggostic proxy Y' that does not reveal the outcome Y. We en-
Counterfactual actribution model
resamplnng
Consuning Videos
Consumingg Videos 2
Cossuning Vidaos 3
Comsnning Vidao
Papiwad
Papsesa -Hi Pieiiad is uplift of Viden in Countrfactuaal attribation
Consuiing Videos
Comaming Videos 2
Consuming
Consuning
User consumption sequeence
Figure I: Counterfactual attribution by touchpoint deletion in the video consumption sequence.
observed through an adversarial, sure scalability and overlap high-cardinality treatment spaces through contrastive learning and calibrated reweighting, and train for stability under personalization and platform dynamics. The result is individualized and reproducible uplift estimates that unify attribution across diverse business tasks.
In summary, this paper presents the following key contributions:
A novel causal MTA framework that addresses unobserved system-level confounders in recom- mendation ecosystems, overcoming a core limitation of prior attribution methods.
A unified strategy for eliminating latent confounding that combines front-door identification, adversarial proxy mediation, and propensity reweighting. This design ensures causal identifiability in the presence of unobserved confounders and prevents outcome leakage by making the mediator observable.
Scalable persapg-reserving operationalization. A deployment-ready learning and evaluation method that maintains positivity and consistency in sparse, larger treatment.
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Strong empirical gains at real-world large-scale recommendation system. ALM-MTA (i) ex- pands attributable coverage by 1.5x over the prior SOTA, (ii) increases daily active users (+0.04%) and daily active creators (+0.6%), (iii) improves unit exposure efficiency by 670%, (iv) achieves higher grouped AUUC across all propensity buckets (maximum gain +0.070), and (v) attains an upload AUC of 0.907 (+40% over SOTA).
2 RELATED WORK
Data-Driven Multi-Touchs Molti-TTuch... Attribution. The central task of MTA is to allocate credit for a conversion across a sequence of user touchpoints. Early data-driven methods moved beyond simple heuristics (e.g., last-touch) to employ interpretable statistical models. Foundational work includes using logistic regression and survival analysis to model conversion probability based on touchpoint history [Shao & Li (2011); Ren et al. (2018)
To better capture the sequential and temporal nature of user journeys, subsequent research adopted path-leve models. Markov chains, which model the probability of transitioning between channels, became a popular approach for quantifying a channel's ' marginal contribution through its removal effect Anderl et al. 42014).
This line of work was later extended with more powerful sequence models like Recurrent Neural Networks (RNNs) to learn more complex user path representations Arava et al.] 2): Ji & Wang (2017). Concur- rently, game-theoretic approaches, particularly the Shapley value, gained widespread adoption due to their axiomatic properties of fairess and additivity Zhao et al. (2018). However, the computational complexity of the exact Shapley value has spurred research into efficient approximation methods Jia et al. (2019): Kumar eet al. (2020). While these methods offer valuable insights, they are predominantly correlational and often rely on the strong, implicit assumption that user touchpoints are exogenous, failing to adequately address the confounding bias inherent in real-world systems.
Causal Inference in Attribution and Recommendation. More recent work has rigorously framed MTA causal effect estimation problem, aiming compute the uplift or Average Treatment Effect (ATE) of a touchpoint on user's conversion probability. A dominant approach for this is the backdoor adjust- ment, rooted in the potential outcome framework Chen (2024). This involves leveraging methods like In- verse Propensity Weighting (IPW) and Doubly Robust (DR) estimators to control for observed confounders Rosenbaum & Rubin (983);; Dudik et al. (2011). These techniques are foundational to modern causal MTA and efforts to debias recommender systems from selection and exposure biases BBotoou et al. (2013); Schnabel et al.] (2016). However, their validity hinges on the strong ignorability (or unconfoundedness) assumption- that all common causes of treatment and outcome have been measured. This assumption is rarely tenable in complex ecosystems like recommender systems, where latent factors such as user intent, social influence (peer effects), and the platform's 's own strategic biases act unobserved confounders Yang et al. 2020);; Molina et al. (2022). Our work directly confronts this critical limitation.
Front-Door and Proxy-Based Methods for Unobserved Confounding. When strong ignorability is vi- olated, alternative identification strategies are required. The front-door criterion, introduced by Neuberg (003) provides another powerful theoretical tool for identifying causal effects in the presence of unob- served confounding, provided a valid mediating variable can be identified. A mediator is a variable that intercepts the causal pathway from treatment outcome entirely. While theoretically elegant, the practical application of the front-door criterion has been limited, primarily due to the stringent requirement of find- ing and perfectly measuring& a mediator that satisfies the necessary conditional independence properties Xu et al.] (20236). Recent efforts have explored using deep learning models to learn mediator representations, but often still assume the mediator is fully observable Luo et al. (2024); Xu et al. (2023c). To overcome the challenge of unobservable variables, researchers have turned to proxy-based and adversarial learning techniques. Proxy variables correlated with latent confounders have been investigated to partially mitigate confounding bias Miao et al. (2018)); Tehetgen Tchetgen et al. (2024a). In parallel, adversarial learning has been adapted for causal inference to learn balanced representations that are invariant to treatment assign- ment, thereby mitigating confounding bias Ganin et al. (001)); Long al.] (017): Pohansson et al.) (2016); Shalit et al.] (2017). However, they have not been systematically integrated with the front-door criterion for MTA. Our work present framework that leverages an adversarial proxy render the latent mediator observable while preventing outcome leakage, thereby operationalizing the front-door criterion for MTA. In contrast to prior proxy-
methods that primarily approximate unobserved confounders to mitigate bias, our design focuses on enabling mediator observation as the key to front-door identifiability. Recent debias- ing methods address latent confounding through multi-cause modeling Huang et al. (2025) or latent causal structure learning Xu et al. (2025), which rely on backdoor-style adjustment. Moreover, including substitute confounders Xu et al. (2023a) method, these approaches do not address front-door identification with latent mediators, outcome leakage from proxies, or positivity in high-cardinality treatments. ALM-MTA fills these gaps through adversarial proxy mediation and contrastive overlap control.
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3 TASK AND PROBLEM FORMULATION
Task. Given a user's recent consumption sequence and contextual features, we estimate the individualized causal uplift of each touchpoint (a consumed item) on the user's subsequent upload (creation) behavior and output this uplift as multi touch attribution for interpretation and downstream ranking. Specifically, let the user level observation be (X, Y); X denotes user and environmental context (including observable plat- form covariates), T = (TI,..... ......,7 .L)) is time-ordered sequence of consumed touchpoints, and Y E {0, 1} indicates whether the user uploads within consumption sequence horizon. The uplift of consumed touch- point is defined as the counterfactual removal drop in upload probability.Sec Appendix {A.1 for a detailed discussion of the front-door assumptions and the proxy mediator.
| 7) = P(Y [[do(T), X) P(Y = (o((T) (T)) X).
(Ty X,
In practice, the model outputs the approximate difference between these two counterfactual probabilities as the attribution strength, producing "multi pointippon" attribution labels that construct actionable touchpoint-level causal attribution targets in non-RCT logs without explicit labels.
Causal Graph Structure and Variables. Large- scale recommendation involves system- and sequence-level confounding which hard to enumerate. To obtain identifiable individual effects, we adopt a graph (X, W, W.T,M, Y) with unobserved confounders W. mediator M. and outcome (see Fig[). X is observable confounding (e.g., static dynamic attributes of users), W is unobservable confounding (e.g., strategies of recommendation systems), . is mediator (e.g., the path trigger the upload action after a user views a video in CDP), Y' is the proxy (e.g., compare the upload with the videos user has viewed and determine the motivated path).
W
X Block the pash
X
M
Y'
Figure 2: Causal graph with latent confounding and adversarially observed mediator. X denotes observed confounding. W is unobserved potential confounding. T means treatment. Y is the result output variable. Y' is observations of result Y. M represents Mediator, which is transmission path be- tween T and Y.
the Causal Graph, the unobserved confounder W and the observed covariates X can both influence T and Y, and the causal path from touchpoints to the outcome must pass through T + M + Y. Since large-scale recommendation system involves hard-to-enumerate system- and sequence-lever confounding, backdoor adjustment alone cannot ensure identifiability.
Front-Door Identification. To identify P(Y 1 X)) and A (X, T) thereby under the observed distribution, we invoke the front-door criterion: (i) Path containment: all directed paths from T to Y pass through M; (ii) No backdoor for M Y: unobserved confounding does not affect M (or is blocked by X); (iii) Block T + M backdoors: by marginalizing on T blocks backdoor paths from T to M. Under these conditions, the standard front-door formula gives:
(do t)) Em EYY = m)) * P(M = m| = =t),
(2)
from which touchpoint-level uplift is defined and estimated.
Overlap under large-cardinality treatments. The meaning of attribution overlap is that for any unit, the probabilities of receiving and not receiving the treatment are strictly positive. When marginalizing over T. the denominator of the conditional probability must be non-zero. However, in the bandwagon business scenario. the treatment space is extremely large, making it difficult to satisfy the overlap condition when marginalizing T.
Objective (individualized attribution). We estimate function go (X that approximates the interven- tional response P(Y = () via front-door identification, integrating the adversarial proxy- mrdittme and contrastive filtering into the estimation pipeline. Touchpoont-level attribution for T, 7 is defined
Á(Ty) XX Y) = 7) go (X, T(y)).
4 MODEL: ALM-MTA FRAMEWORK
ALM-MTA (Fig can be viewed as a practical front-door generalization of causal multi-touch attribution to large-scale recommendation, taking a user context X and a time-ordered treatment sequence T to pro- duce individualized, touchpoint-leve uplift labels. Operationally, the model first reweights observational logs with inverse propensity scores (IPW) to mitigate back-door bias from X - T. A shared neural back- bone then encodes the sequence and feeds two coordinated heads. The first is a lightweight ITE head that aggregates touchpoint embeddings with learned weights to parameterize the logit for P(Y|X, T). Uplift is obtained by comparing this logit with the one re-estimated after removing a touchpoint. The second is a
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mediator-observation head supervised by a proxy Y' Through adversarial learning, this head is trained to capture the T + M + Y pathway while suppressing shortcut leakage Y' Y so that the mediator signal remains usable for front-door identification.
P(P)2)
P(P
add
Deduct residual Y
minus
multilyy
weighteed sum
Adversiiial I. coming
Share NN
IPW
Melistor:: Observation of
user Reature (user id, ags, gender etc.)
2
Dreatment
Figure 3: The ALM-MTA architecture. User features and treatment sequences are reweighted via IPW and encoded by shared backbone. A weighted ITE head estimates per-touchpoint uplift, while an adversarial proxy branch renders the latent mediator observable without leaking the outcome. Residual corrections refine signals. jointly enabling front-door identification and stable counterfactual attribution at scale.
4.1 THE STRATEGIES FOR ELIMINATING LATENT CONFOUNDING
major challenge in multi-toude attribution in observational logs is the presence of latent confounders that simultaneously affect the consumed touchpoints T and the upload result Y. Without proper treatment, these confounders lead to biased attribution estimates. We adopt three strategies, including front-door identifica- tion, adversarial proxy mediation, and propensity reweighting, to eliminate such confounding and guarantee causal identifiability.
Eliminating latent confounding via the front-door criterion. As shown in Section 3, the causal graph in Figure fully satisfies the three conditions of the front-door criterion. Therefore, we can apply the front- door criterion to eliminate unobserved confounders W. When conditioning on treatment T and covariates X, the mediator M is not confounded with the result Y. Formally,
IdoM = m)] = Σn _ E| = m, T = t', X x) P(T = =t, x =x).
(4)
Given T and X, all back-door paths from M to Y are blocked, that [|Y m., t X x| E|Y|M m.T = = z|
(5)
Ely]do(M- mm] Er E E M m, -, P(T, X do( (M = m))
(6)
= E m,,==t,X' 22 P(T,
Substituting this equality into the front-door formula yields the following.
do = t)] = Em,t' E|Y M m, T f', x
(7)
x P(T = f", X = x)) P(M m|7 =t)
This derivation shows that the causal effect of T on Y can be identified through the mediator M, even in the presence of unobserved confounders W.
Mediator Identification via Adversarial Proxy. We posit that the causal effect of T on Y is transmitted through a latent mediator M. However, M is unobservable in practice. We therefore introduce proxy variable Y' that correlates with M. mediator branch is supervised by Y' to produce an embedding M To prevent shortcut leakage, we add an adversarial objectivdBai et al.] (202I): a discriminator attempts to predict Y from M, while the mediator branch is optimized to suppress this predictability. As result, M retains only the information necessary for the causal path + - Y. without directly revealing the outcome. Observing the proxy brings an additional benefit. By the monotonicity of conditional variance, enlarging the conditioning set from (X,T) to (X, T,Y) satisfies:
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Var(Y|X, 2 Var(Y|X,T,YY),
(8)
which shows that incorporating Y' reduces the variance in the estimation of uplift and improves stability in high-cardinality treatments. This is particularly valuable in high-cardinality treatments, where data per touchpoint is sparse.
Unconfoundedness under the observational distribution. Data-driven modeling must rely on historical exposure logs; because exposures are jointly determined by preferences and platform policies, the in- terventional path do(T") deviates significantly from the empirical log ( T, M, Y). Direct training on the raw data therefore learns correlation and is affected by confounding. To bridge this gap using observa- tional data alone, we adopt "front-door debiasing + inverse- proppesitt reweighting" scheme: assign each sample the weight w(x,t) t) = a/s (T' X = z). Under positivity and a correctly specified propensity model, the reweighted distribution becomes "as if randomized" given X; further leveraging the front-door structure - M Y (which, after conditioning on T, X, blocks all back-door paths from M to Y), the upload probability can be decomposed into counterfactual gains at the touchpoint level that are estimable for derivation):
from observables (see Appendix $A.2
upload=
Σ uplift,
ΣEY
E|Y do(T= = E do(T = =0]
Pobe (Ix, m. Torigin)
ΣEEr f(m, t,
Σ ((M,I,X)x(XX1))
Po x)
instance
Where J(M,T,X) E|y X). It follows that combining front-door debiasing with IPW enables causal attribution to be identified directly from raw logs, guarantees unconfoundedness with respect to latent confounders, and establishes the scheme's 's theoretical implementability. The loss in upload probability is modeled as the uplift, which is assumed to be linearly aggregable into the overall upload.
4.2 CONTRASTIVE LEARNING FOR OVERLAP
For causal identification, every unit must have non-zero probability of receiving each treatment. In large- scale attribution, however, the treatment space is extremely high-cardinality. and many touchpoints occur rarely. This leads to violations of positivity, making marginalization over all 7 unstable and high-variance. Therefore, we introduce a contrastive learning module that estimates the semantic 'match' between each consumed touchpoint T and the proxy mediator Y', as shown in Fig 4 Positive pairs are constructed from the actual proxy-touchpoint interactions that precede upload, while negatives are drawn from unrelated touchpoints. The InfoNCE loss encourages embeddings of (Y', T) pairs that are causally related to be- ing closer, and unrelated pairs to be farther apart. The resulting score w(T, Y') quantifies how strongly touchpoint is aligned with the proxy outcome. During front-door estimation, we restrict marginalization the high-match subset Thigh {r E TW, top K), and reweight accordingly. This guarantees that the denominator of conditional probabilities remains positive, satisfying overlap and reducing estimator variance.
4.3 STABILITY-ORIENEED ATTRIBUTION
weighted sum
Causal conclusions should not fluctuate simply because of incidental variations in training. When the same statisti- cal procedure is applied, the resulting causal effeetts ought to remain invariant. In practice, models trained on large- scale personalized logs are highly sensitive that uplift timates often drift across random seeds, data orderings, and minor perturbations, undermining the reproducibility of attribution. Our framework explicitly enforces stability by designing the learning architecture and training process to resist such sources of randomness. Instead of allow- ing the network absorb all correlations present in the data, we deliberately simplify the feature interactions so that only robust signals are retained. This structural par- simony is further reinforced by L2 regularization, which prevents the model from overfitting spurious fluctuations.
contrustive
similarity
Myy
Share Gate
Encoder
Ticotment
Observation otY
Figure 4: Contrastive overlap control for front- door estimation.

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5 EXPERIMENTS
5.1 BASELINE MODEL
We benchmark three families of methods on the Criteo conversion- cnnversion-predcttoon task: statistical learning (SL), deep learning (DL), and causal learning (CL). Logistic Regression (LR) serves as the classical statistical learning baseline. For deep learning, we include deepMTA Neuberg (0033) as a representative attribution model. We evaluate three causal learning baselines: DML.Chernozhun et al. (2018), DESCN et al.] (2022), and causalMTA Liu et al. (2025a). All baselines are trained on the same data and features, using identical splits and evaluation protocols for fair comparison.
5.2 EXPERIMENT SETUP
The model comprises a counterfactual attribution backbone adversarial- and contrastive-leaming branches. The backbone estimates per-touchpoint uplift under deletion/ppetturhationn on list-wise sequences and auxil- iary branches perform proxy mediation and cross-modal alignment between consumed items and produced content. The per-forward complexity is I.31B FLOPs in total: 0.92B FLOPs for the counterfactual attribu- tion backbone and 0.39B FLOPs for adversarial and contrastive components. Path-level attribution signals are subsequently converted into point-wise targets for scalable distributed training. Simultaneously, we use the Direct Routing Gradien(Liu et al.] (2025b) method to reduce the impact of tradeoff's between targets. Further implementation details and the full training recipe are provided in Appendix A.3
5.3 DATA
Our experiments are conducted on large-scale user consumption-proouution logs. To simulate the streaming nature of online systems, adopt streaming training method with both positive (upload) and negative (non-upload) samples.
Data volume. We used 100 consecutive days of online logs to generate over 30 billion training instances. Treatments were treated as short video touchpoints within the recommendation system that could potentially promote creation or increase activity, organized into consumption sequences based on user chronological order. The number of treatment candidates reached the billion-level.
Labels. The outcome variable Y is binary upload signal (0/1). To enrich supervision without leaking Y, we introduce a proxy signal Y' in the CDP scenario. Specifically, Y' measures the similarity between the user's 's new work and the candidate treatment according to pre-defined strategies and rules. By design, Y' acts as an adversarially constrained, observable intermediary proxy that provides the necessary path information for front-door identification.
Positive and Negative sampling strategy. Positives are users who uploaded contents. The overall positive- negative sample ratio was controlled at 2:3 to ensure balanced classification and statistical power. We ensure the interaction volume of negatives, quantified by the length of collect and like sequences. The upload activity distribution of negatives is aligned with that of the positives. The specific sampling strategy shown in Table
Streaming training. We construct a Hive table in list-wise format every hour. Stratified negative samples are used to control computational cost while preserving representativeness, and fea- ture generation is performed via the Kai- works FG pipeline. During inference process, the finerranking model queries the MTA model to obtain attribution labels, which are transformed
Table 1: The specific positive and Negative sampling strategy.
L
POSITIVE
NEGATIVE
RATIO
< 20
5 billion
4,440 billion
0.16%
20 < L 120
3 billion
840 billion
0.54%
L> 120
2 billion
280 billion
1.07%
into point-wise samples with full shuffling for training stability.
5.4 CAUSALITY EVALUATION SCHEME
Since randomized controlled trials (RCTs) are not available, the true average treatment effect (ATE) cannot be directly observed. Standard predictive metrics such as AUC only assess discrimination accuracy but fail to measure whether the predicted uplifts correspond to genuine causal contributions. To bridge this challenge, we designed a non-personalized AUUC protocol based on Shapley value sampling, which provides a princi- pled evaluation of attribution quality observational data. Appendix $A.4showw the full non-personalized bucketed AUUC protocol.
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5.5 ESFECTIVENESS VERIFICATION
Ablation Studies and Mechanism Analysis. We ablate the causal pipeline to quantify the contributions of mediator observation and contrastive adaptation. First, a DML counterfactual baseline with propensity correction and privileged inputs achieves an AUC of only 0.6498 and UAUC of 0.50, indicating that latent system-level confounding remains unresolved by backdoor adjustment alone. Second, while direct mediator observation through Y' inflates discrimination (AUC 0.97, UAUC 0.90), it induces severe outcome leak- age and training instability, as evidenced by large loss oscillations and failure to converge (Fig- a()). By introducing adversarial learning, ALM-MTA effectively eliminates this leakage and stabilizes convergence, yielding a robust AUC of 0.86 and UAUC of 0.71 (Fig. [b)) Furthermore, integrating a MoCo-style con- trastive learner addresses the sparsity in large-scale treatment spaces by ensuring overlap, which further boosts predictive accuracy to AUC 0.907 and UAUC 0.825. Finally, for established attribution categories. the method improves AUC to 0.63 0.70.
be Dieere
Figure 5: Training dynamics and ablation analysis. (a) Direct proxy observation leads to non-converging oscillation, indicating outcome leakage. (b) Adversarial learning removes leakage, resulting in stable con- vergence. (c) Loss comparison with baselines, where ALM-MTA shows higher initial loss (due to MTL) but converges effectively. (d) AUC comparison, demonstrating that ALM-MTA achieves superior discrimina- tion against baselines as training progresses.
Results and discussion. We evalu- ated the performance of all methods us- ing AUC, log loss, gAUC and aver- aged grouped AUUC. Grouped AUUC measures causal validity by ranking pre- dicted uplifts within propensity-stratinn treatment-cluster buckets and integrating the uplift curve curve-larger area indicates better alignment with realized causal gains. Appendix $A.4 shows the full non-personalized bucketed protocol. Lo- gistic regression performs poorly under observational confounding. with AUC 0.5102, log loss 0.7833, gAUC 0.5011, and grouped AUUC 0.4725. Deep sequence models such as deepMTA improve discrimination to AUC 0.7598 with lower log loss 0.4108, yet their gAUC 0.6364 and grouped AUUC 0.6277 reveal sensitivity to spurious sequential correlations. DML and DESCN show uneven behavior on ordered, sparse treatments: discrimination remains low (AUC 0.6498 and 0.5492: gAUC 0.5031 and 0.5003) despite higher grouped AUUC around 0.84, indicating unstable calibration and ranking consistency. CausalMTA introduces coun- terfactual deconfounding and improves AUC 0.8167, log loss 0.2835, gAUC 0.6752, and grouped AUUC 0.8493, though it still depends on observable confounders. Our ALM MTA attains the best results across all metrics, reaching AUC 0.9070, the lowest log loss 0. 1384, gAUC 0.8210, and the highest grouped AUUC 0.8686, as shown in Table aableeaand and visualized in Fig. d). The consistent gains across discrimination, calibra- tion, and grouped causal ranking indicate that adversarial mediator observation and contrastive adaptation effectively remove latent confounding and scale to large treatment spaces, yielding reliable and causally faithful attribution.
Table 2:
Methods performance across discrimination, calibration and causal ranking metries. AUC and gAUC assess discrimina- tion; log-loss reflects calibration; avg AUUC measures grouped causal uplift ranking. ALM-MTA attains the best results on all four metrics.
AUC
log-loss
gAUC
avg AUUC
LR deepMTA
0.5102
0.7833
0.5011
0,4725
0.7598
0.4108
0,6364
0.6277
DML
0.6498
0.5433
0.5031
0,8421
DESCN
0.5492
0.6920
0.5003
0.8429
causalMTA
0.8167
0.2835
0.6752
0.8493
ALM-MTA
0.9070
0.1384
0,8210
0.8686
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Stability Analysis. Fig. Gillustrates the stability of attribution estimates across DML, DESCN, CausalMTA and our ALM-MTA. We exclude IR and DeepMTA as they capture only correlational patterns rather than causal effects. We observe that both DML and DESCN exhibit high sensitivity to random initialization, with markedly divergent bimodal pxtr distributions across training runs, leading to inconsistent causal ex- planations. This instability underscores their inability to provide reproducible causal insights in large-scale recommendation settings. CausalMTA reduces this variance to some extent, yielding more consistent distri- butions and thus partially improving attribution stability. By contrast, ALM-MTA achieves the most stable behavior: its pxtr distributions remain highly consistent across seeds, indicating that the causal attributions it generates are reproducible and robust. This confirms that the adversarial mediator design and stability- oriented training procedure effectively mitigate randommess in estimation, yielding stable and interpretable causal attributions.
5.6 ONLINE EVALUATION AT REAL- WORLD PRODUCTION SCALE
We deployed ALM-MTA in real-world production, where its multi-touch uplift labels are consumed by fine-ranking training and online reweighted serving under fully aligned policies. We evaluate four metries to ensure causal coherence and reproducibility: supply-side gains, scalability, coverage, and attribution accuracy.
Supply-side gains with steady platform health. Our first online experiment confirms that the proposed method robustly improves key supply-side metries. We observe a direct increase the core active creator population, with Daily Active Users (DAU) rising by +0.04%. The addressable scale of these creators also expands by 119%.. These gains are mediated by stronger creation-intent signals, as theorized. Engage- ments with production-related prompts (+0.292%), cublication-focusee prompts (+0.789%) and social- driven prompts (+0.465%) all increase significantly. This strengthening of the mediator stage (M) leads to downstream increases in producer WAU (+0.566%) and trend-following creators (+0.555%) alongside a +0.065% improvement in content diversity. Importantly, these targeted improvements do not negatively im- pact overall platform health. Macro-level indicators such active devices (-0.010%) per-user time spent (+0.018%) 8%), and per-device time spent (+0.010%) fluctuate only negligibly. This provides strong evidence that our front-door and adversarial-mediato architecture effectively strengthens the intended T M - Y causal chain while preventing reliance on spurious correlations.
Scalability in larger treatment spaces. Under heavier load and larger treatment spaces, the method pre- serves positivity and remains stable at system scale. Producer WAU and author-open scale increase by +0.145% and +0.179% respectively. The CDP indicators also rise significantly, with production traffic effi- ciency at +0.00%% and uploads per user at +-0. 0.56%%. Stronger consumption-side signals strict-bandwaan VV +0.424%, wondwbban plays +0.249%) validate the maintenance of overlap by contrastive front- door marginalization. Platform health remains within narrow fluctuation band (active devices +0.035%, overall time +..00%)) and faster interaction scenarios show enhanced comment dwell (+0.128%), indicat- ing that expanding the treatment space does not come at the expense of platform health.
Coverage and multi-touch depth. Under a unified threshold of 0.54, upload coverage increases from 0.39 to 0.58 (up to 1.49x), while the average number of attributed touchpoints per covered upload rises from 2.93 to 7.31 (up to 2.49x). These results indicate that, in high-cardinality treatment spaces, an attribution framework incorporating front-door identification and contrastive learning can substantially broaden the coverage of explainable uploads and increase touchpoint-level explanatory granularity. The resulting higher label density directly improves supervision quality for uplift-oriented re-ranking and incentive allocation.
Attribution accuracy. Under the 'matching and activation' criterion, we assess causal links between touch- points and uploads. Compared with the method currently deployed online (Precision 4.32%, Recall 7.95%). our method achieves substantial improvements under the same evaluation: Precision increases to 21.88%, and Recall increases to 11.67%. Moreover, in the high-confidence segment (top-score bucket), Precision reaches 54%, indicating strong discriminability and calibration in the head region of the scoring function. Overall, ALM-MTA produces attribution labels with markedly higher accuracy and practical utility, provid- ing more reliable supervision for downstream uplift re-ranking and incentive allocation.
Overall, the online results across these metrics are consistent with offline evaluation and ablation studies. These findings validate the effectiveness of our method's 's deconfounding strategy and yield attribution labels that are immediately actionable for production ranking and incentive design. The Appendix $A.6 presents diverse and cross domain attribution case studies to demonstrate the universality of our causal attributions.
6 CONCLUSION AND FUTURE WORK
Conclusion. We present ALM-MTA, an extensible and practical causal attribution framework for cotumpttion-divve--podece platforms. The method (i) casts attribution as a front-door identifiable prob- lem with latent mediator that transmits the causal pathway from consumption to upload, (ii) renders the
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0454
leat 1200
-
Figure 6: Attribution stability analysis across random seeds. We compare the distribution of attribution scores (pztr) under identical training data but varying random seeds (10, 100, 1000). Baselines (DML, DESCN) exhibit dispersed distributions across seeds, indicating high sensitivity to initialization. In con- trast, ALM-MTA demonstrates highly consistent overlap, confirming superior reproducibility and robust- ness against random factors.
mediator observable via an adversarially constrained proxy Y' that is invariant to the outcome Y, coupled with contrastive learning, and (iii) ensures scalability and positivity in high-cardinality treatment spaces by restricting front-door marginalization to contrastively identified high-match consumption-uplott pairs. Together, these elements remove latent confounding without outcome leakage and yield reproducible, in- dividualized uplift labels with materially higher actionable coverage and label density at production scale. Empirically, ALM-MTA improves predictive accuracy and grouped causal ranking offline and delivers mea- surable gains to creator-ecosystem metrics online while preserving overall platform health.
optimal balance between coverage and precision, thereby maximizing attribution efficiency; (ii) Relaxing the independence assumption via enhanced training architectures (e.g., multi-head attribution) and leave-one/two-our inference to capture higher-order depen- dencies; and(iii) Improving attribution explainability by elucidating the causal mechanisms linking sumption to production and extending the framework to broader feedback and consumption loops.
Future work. (i) Threshold analysis to determine
ACKNOWLEDGEMENTS
We thank Huidong Bai for helpful discussions. Authors Yuguang Liu, Luyao Xia and Hu Liu contributed equally to this work.
REPRODUCTBILITY STATEMENT
For community verification and reproducibility, we have released and open- sourcse the full codebase in the link In addition, we will release a de-identified version of the CDP dataset together with the preprocessing scripts in link of github later, so that the complete workflow (from data construction to evaluation) can be fully reproduced. Model design and identification are specified in Sec. covering IPW reweighting, the ITE head, and the adversarial mrexy-mediator used for front-door identification (Sec. 4.1p. Architectural complexity and compute (FLOPs) are reported in Sec. [.22 and matched by our configs. Data construction and preprocessing (streaming training, positive/negative sampling, and the proxy label Y' are documented Sec. [5.) Offline metrics include AUC, log-loss, gAUC, and propensity-strad strrafffed grouped-AUUC: the AUUC definition and the full personnnnal bucketed protocol given in Sec. [5.4 and App. [A.4] and are reproduced our evaluation toolkit. To demonstrate reproducibility, we report multi-seed stability; our method consistently regenerates the stability plots in Fig.
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THE USE OF LARGE LANGUAGE MODEL
We used large language models (LLMs) only to aid polish the writing; no content generation, ideation, data analysis was performed by LLMs.
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A APPENDIX
A. PROXY-BASED JUSTIFICATION OF THE MEDIATOR Y'
our CDP setting, the latent mediator M represents an "inspiration" or creative-intent signal that connects content consumption to subsequent uploads. Since M is not directly observed, we follow the proxy iden- tification literature Miao et al. (0188); Tehetgen Tchetgen et al. (2024b) and use a proxy variable Y' as a surrogate for M. The proxy Y' is not arbitrary auxiliary label; it defined by strong business rules that reflect how consumption can inspire production in our platform. Concretely, Y' indicates whether a con- sumed video and subsequently uploaded video (i) use the same music template, or (ii) are highly similar an embedding space, capturing similar themes or storylines. Before this work, our production system already used y' directly heuristic link from consumption to production. Empirically, strict element- level matching (same music/template) covers about 7% of daily uploads, and broader semantic matching contributes roughly another 40%, so the proxy-based inspiration paths account for substantial portion of creator activity.
Following the proxy-identificann framework of Miao al. and Tchetgen Tchetgen et al., an effective proxy for an unobserved mediator should satisfy three key conditions: (i) relevance condition, meaning the proxy is strongly correlated with the latent mediator M: (ii) an exclusion condition, meaning that conditional on M the proxy does not have a direct path to the outcome Y; and (iii) an identifiability condition, meaning the proxy exhibits sufficient variation to capture changes in AM and allow the causal effect to be identified. In our design, the business rules underlying Y' ensure the relevance and identifiability conditions: Y' is explicitly constructed to track changes in "inspiration" (e.g., reusing music/templates or themes), and the high coverage figures above indicate rich variation across users and content.
To approximate the exclusion restriction and avoid introducing new shortcut path from Y' to Y, we employ adversarial mediator learning. The media- tor branch is trained to predict Y', while discrimi- nator simultaneously tries to predict from the me- diator representation. The mediator network is opti- mized (via a gradient-revergg diction impossible, effectively suppressing any infor- mation about Y that can be directly recovered from the learned mediator. In this way, the raw proxy Y' is used to construct "clean" mediator representation that remains strongly related to M but has minimal direct predictive power for Y.
W
X
layer) to make this pre-
T
M
Y
r'
Figure 7: Minimum DAG.
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Finally, we connect this proxy-mediator construction to the front-door criterion discussed in Section 3. Our causal graph (Figure 2) involves variables (X, W, M,,), Y). where X are observed covariates, T are con- sumed touchpoints, M is the latent "inspiration" mediator, Y is the upload event, and W denotes unobserved system-leved confounders (e.g., latent intent or social influence). The minimum DAG graph is as F The standard front-door identification of the effect of ' on Y via M relies on three conditions: (i) all directed paths from T to go through M (path containment); (ii) there no uncontrolled confounder affecting both M and Y, i.. M TL Y T. (no M + Y back-door); and (iii) there is no uncontrolled confounder affecting both T and M, i.. TL M X (no + M back-door).
In large-scale recommendation platforms, Condition (ii) is the most fragile in the raw variables: observed factor W can influence both the mediator .M and the outcome Y. opening a back- door path M 4- W - Y and inducing spurious association between M and Y even after conditioning on T and X. The combination of the proxy Y" and adversarial mediator learning is designed precisely to approximate this condition at the representation level. The learned mediator representation M retains the variation needed to explain Y' and the T + M + Y pathway (relevance), while the adversarial objective actively suppresses any shortcut information about Y that leaks through W. Thus, when we apply the front-door adjustment in Section 3, M should be understood as a "cleaned" surrogate mediator that approximately satisfies the "no M - Y back-door" requirement in our CDP setting.
The combination of the proxy Y" and adversarial mediator learning is designed precisely to approximate this condition at the representation level. The learned mediator representation M retains the variation needed to explain Y' and the T - M - Y pathway (relevance), while the adversarial objective actively suppresses any shortcut information about Y that leaks through W. Thus, when apply the front-door adjustment in Section 3, M should be understood as a "cleaned" surrogate mediator that approximately satisfies the "no M Y back-door" requirement in our CDP setting.
Proxy sensitivity analysis. We also analyse how the quality of the proxy Y' affects both offline causal metrics and online KPIs. Since M is latent, we cannot directly measure corr(Y', M). Instead, we rely on an existing "inspiration" module in our CDP system that models M as a function of (T. Y') and is regularly audited by third-party annotators. For each inferred inspiration path, annotators judge whether the consumed content plausibly inspired the upload. From these audits we obtain (i) path coverage (the fraction of uploads with at least one inspiration path) and (ii) precision (the fraction of paths judged truly inspirational), which together serve as a qualitative proxy for the correlation between Y' and the latent mediator M.
To study sensitivity, we start from very strict definition of Y' and gradually add more proxy signals along business-defined paths (an "add-one" strategy). We consider four nested configurations:
Strict element match. Y' is defined only by exact element matches (same music template, near-duplicate text). This yields path coverage of about 16.8% with precision close to 100%. In this configuration, ALM-MTA achieves grouped gAUUC 0.8496 and an uplift in creator DAU of about +0 +0.201% + 0.123% ss +0.32%.
Element + semantic match. We extend Y' include high semantic similarity in embedding space in addition to exact element overlap. Coverage increases to roughly 28.8% with precision around 82%, and grouped gAUUC improves to 0.8573, with an additional creator-DAd DAU uplift of about +0.164%.
Element + semantic + path. We further incorporate path-level signals (e.g., collect-and-reuse patterns). Coverage rises to about 39.8% with precision around 88%, and grouped gAUUC is R 0.8577, with an additional DAU uplift about 10.10%%
Full path set. Finally, we use the full suite of proxy signals. Coverage reaches about 60.4% with precision around 90%, and ALM-MTA achieves grouped gAUUC z 0.8686. In this deployed configuration. the overall uplift in creator DAU is about +0.514% compared to the rase- baseline, which can be decomposed into incremental gains of roughly -0. -0.222% and 10.160% when the last proxy families are added.
We observe a clear, smooth pattern: when we deliberately weaken the proxy (using only very strict element matches), both gAUUC and creator-DAU gains are reduced but do not collapse. As we enrich y' with additional semantic and path-level signals, coverage and precision remain high, and both gAUUC and online uplift improve monotonically. This behaviour indicates that ALM-MTA is robust to reasonable variations in proxy quality: long as Y' remains a meaningful proxy for inspiration, the front-door mechanism continues to deliver consistent causal and business gains.
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A.2 PROOF OF UNCONTOUNDEDNESS DERIVATION
We provide the detailed derivation for equation 10
Starting from the interventional definition:
upload = Σ uplift
(10)
B|Y - =)] Σ B|Y
- = t, (000==))___0MMmm---106000- - = -[ = =x).
(11)
By the front-door criterion, conditioning on T and X blocks all back-door paths from M to Y. Therefore, we can rewrite:
B|y do(T [)] ES(M m, T 1, x- x) P(M = T 1, = x) P(X x),,
(12)
where ((M,T,x)= F M,T,XJ.
Using observational data, the joint distribution can be reweighted via inverse propensity:
P(X,M,T) (obs(X, M, Torigia)
(13)
Pabs X)
Substituting, we obtain:
Pobs (I. m, Torigin)
F|Y do(T = =0] Σ [(m, x.
(14)
Poba { x)
Aggregating across treatments yields the full attribution decomposition:
upload = ΣEY do(T = t)).
E|Y
(15)
This confirms that combining front-door adjustment with IPW produces an "as-if randomized" distribution, enabling unbiased causal attribution directly from observational logs.
A.3 IMPLEMENTATION DETAILS AND TRAINING RECIPE
Model architecture. ALM-MTA is implemented as a multi-task model with a shared backbone encoder fo(X,T) T) and several task-specific heads: (i) main head mmain that predicts upload Y and uplift; (ii) proxy head proxy that predicts the proxy Y and produces a mediator representation M; (iii) an adversarial head hadv that predicts from M; (iv) DML-related components that reweight the main loss by the esti- mated propensity w(X, T); and (v) contrastive head 1 that operates on (T, Y') embeddings for in-batch pairwise contrastive learning. Gradients from explanation-related heads (proxy, adversarial, contrastive) to the backbone routed using a hhop-gradient/ gradient-surgee surgery scheme that uplift prediction remains the primary optimization objective.
Optimization. Dense parameters are optimized with Adam (initial learning rate 10 10-5), and sparse embedding parameters with Adagrad (initial learning rate 5 x 10-4), We apply standard L2 regularization on all trainable parameters. Unless otherwise stated, models are trained with large mini-batcher under a data- aata-parallel setup over multiple workers.
Losses and weighting. Let Limann denote the main loss on Y (uplift / upload), LDML the DML-related regularization, Ladv the adversarial loss on Y given M, Leeg the sum of regularization terms, and Lete the in-batch contrastive loss. The total objective is
Lmain + ADMLLDML + Audy + Aregbeeg + Aetr
In practice, choose coefficients that the average loss scales satisfy
Lmain LDML Ladv Lreg
Lete RS 1 0.6 4 0.1
Concretely, we use a small base weight (e.g., 10-3) for the contrastive term and adjust ADML, Audvs and Areg such that the monitored loss magnitudes fall into the above ratio. This keeps gradient contributions from different objectives within the same order of magnitude, which we find sufficient for stable training industrial scale.
Moreover, our empirical tuning guideline is that the different loss terms should have similar magnitude on the training curves (within order of magnitude). Under strong regularization and at industrial scale, we find that the method is quite insensitive to small perturbations of these hyperparameters((ee Fig 8
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Figure Parameter sensitivity analysis. (a) The changes the learning rates dense and sparse parameters have limited impact on AUC level at convergence, verifying the insensitivity to the Ir hyperparameter. (b) The changes in weight of regularization have limited impact on AUC. (c) The changes in weight of con- trastive learningloss have limited impact on AUC.
Training schedule. We adopt lightweight staged schedule:
Backbone warm-up. Train the shared backbone and main head with DML. (i.e., Immain + TDML) until the offline AUC curve becomes stable.
(ii) Proxy co-training. Enable the proxy head and jointly optimize Tmaan and the proxy loss so that the mediator representation M captures Y'-related signal, while still treating Lmain the primary objective.
(iii) Adversarial and contrastive annealing. Starting from zero weights, gradually increase Andv and Aetr to their target values. This avoids an over-strong adversary contrastive term at early stages and keeps Lmain dominant; in practice we monitor that all loss terms remain within roughly one order of magnitude.
A.4 OFLINE CAUSALITY EVALUATION: PROPENSITY EOPPENSITY-STRATTIIIED GROUPED AUUC
We first cluster the cardinaadiy touchpoints into approximately K 1000 categories using multimodal embeddings, denoted as T". This reduces sparsity and enables reliable estimation of uplift at the cluster level. For each treatment cluster, we estimate its causal contribution using unbiased Shapley value estimator. Instead of exhaustively computing all marginal contributions, we apply sampling:
1 SK =(S (i)) -(0))-v(3 ((((())
d.(1) = K
-
(()
(16)
where 5(() is a randomly sampled subset for participant in the k-th sampling, and v(-) is the model's value function (expected outcome). (()())))) denotes the probability of sampling the subset 5(k) conditioned on including participant i. The resulting di is The estimated value of the Shapley value for participant i.
To further reduce bias, stratify
by estimated propensity scores P(T'IX XX). This ensures that compar- isons between exposed and unexposed units within each stratum are balanced, mitigating residual confound- ing. Finally, the Area Under the Uplift Curve (AUUC) computed by aggregating Shapley-base uplifts across users and treatments:
AUUC N .N' -== E ftliftni:
(17)
where ypliflns is the simplified Shapley value assigned treatment i for AUUC evaluates whether higher predicted uplifts correspond to higher realized causal gains, thus capturing the causal validity of attribution.
Goal. In non-RCT logs, absolute ATE is unobservable and naive correlational metrics (e.g., AUC) do not validate causal usefulness. We therefore design a non-personalized bucketed protocol that approximates causal uplift at the treatment-cluster level and evaluates whether model's predicted uplifts align with these estimates.
Setup. Let be users, T be consumed touchpoints, and the upload outcome. We cluster touchpoints by multimodal content embeddings into K treatment clusters C {1, ... K} (typical K =s 103). For each user i and cluster c, define an exposure indicator Ei,e E (0, 1} (user consumed any item from cluster e in the window). Let X; denote user/context covariates. model under test outputs predicted uplifts úi.--
Propensity estimation & stratification (PSM-style debiasing). To mitigate user- preference bias that in- validates purely data-driven ground truth, we estimate exposure propensity for each (i, c) pair,
6 = P(E,.e = Xt),
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using a simple, leakage-free classifier (e.g., logistic regression). We stratify (i, pairs into B propensity buckets by quantiles of et,e (e.g.. B 10). Within each bucket units have comparable exposure likelihoods, reducing static/dynamic preference bias and enabling fair comparisons.
Shapley-bare based uplift labeling within buckets. Within each bucket b, we estimate per-pair causal contri- bution using simplified Shapley sampler that treats treatment clusters as players:
(((S)) Σ L I.
1
(c)) v((()) \ Nco)) Pr(S(() ())
(18)
where S(O) is random subset of clusters preceding e in a random permutation and v;(-) is the user-level value function (expected upload under the subset). This produces de-biased, bucket-conditional uplift labels that are more robust than Shapley alone.
Grouped AUUC. For each bucket 6, sort pairs (i, c) by model scores úن, in descending order, obtaining the sequence (,(,,)))).,(1))))))))) (To.(N)) (o,(N.))) Define the cumulative causal gain
h Ub(k)) = Σ
200)
h I..,...,NN
Pis.n)
1=1
and normalize by the total uplift Z, = ΣØ= (inde the normalized curve:
((0)
The bucket-level AUUC is the Riemann sum of
1
Us(k) Z
AUUC,
(19)
No
Finally, the grouped AUUC aggregates across buckets with size weights wi /o/ Ev Ny:
gAUUC- Σ wh AUUC
(20)
Higher gAUUC indicates better alignment of predicted uplifts with bucket-conditional causal gains.
Why this improves offline evaluation. (i) De-biasing: Shapley alone is data-driven and suffers from user-preference bias; propensity stratification (PSM-style) removes major static/dynamic bias, making the Shapley labels usable as proxy ground truth. (ii) Positivity: Bucketing enforces overlap by comparing units with similar propensities, stabilizing estimates in high-cardinality treatment spaces. (îii) Comparability: Ranking within buckets prevents models from exploiting exposure frequency artifacts and focuses evaluation causal ordering quality.
A.5 SUPPLEMENTARY EXPERIMENTS ON PUBLIC CRITEO DATASET
Criteo (Criteo Attribution Modeling for Bidding Dataset) datase Eustache, Meynet Julien et al. (2017) represents a sample of 30 days of Criteo live traffic data. Each line corresponds one impression (a banner) that was displayed to a user. For each banner we have detailed information about the context, if it was clicked, if it led to a conversion and if it led to a conversion that was attributed Criteo or not. Data has been sub-sampled and anonymized as not to disclose proprietary elements. The average touchpoint sequence length is 2.68, the longest touchpoint sequence length is 880.
Using cost (bucketing value, which makes modeling easier to converge and further reduces information leakage) as Y', adversarial learning is used together with impressions to eliminate shortcuts from T- > Y and Y > Y'. The variables after adversarial learning are used observations of M; the longest sequence length is set to 16.
To ensure stability, the model incor- porates adversarial learning, resulting in a larger initial loss and thus a higher logloss after convergence com- pared to causalMTA. ALM-MTA models the transforma- tion better (i.e., has a higher AUC). Furthermore, the con- fidence intervals of ALM-MTA are more stable, primar- ily due to further optimization the stability component. To some extent, ALM-MTA exhibits better interpretability and robustness than other baseline models, yield- ing more stable causal conclusions (see Table
AUC and logloss:
Table 3: AUC and log-loss between ALM-MTA and causalMTA.
AUC
log-loss
causalMTA
0.9659=0.01 0.9729#0.01
0.0517+0.003 0.0634+0.002
ALM-MTA
17

---

Budget and CVR: Because of the introduction of cpp/attribute (adversarial learning leakage prevention) front-door observation signal, ALM-MTA more sensitive cost/attribute and can achieve better CPA. For CVR, the improvement is limited at 1/2 budget, but results in 1/4, 1/8 and 1/16 budget illustrate that ALM-MTA is more sensitive cost, especially when the budget is limited (see Table
Table 4: Performance comparison under different budget constraints.
Model
CPA
Conversion
CVR
1/2
1/4
1/8
1/16
1/2
1/4
1/8
1/16
1/2
1/4
1/8
1/16
DeepMTA
36,25
30.60
26.08
25.97
1372
880
549
289
0.1194
0.1202
0.1236
0.1249
CAMTA
32.61
29.73
26.05
26.25
1270
864
538
211
0.1127
0.1160
0.1191
0.1166
CausalMTA
30.34
29.52
26.45
25.47
1441
976
548
255
0.1247
0.1265
0.1305
0.1283
ALM-MTA
27.32
24.85
22.71
22.03
1503
1070
557
291
0.1248
0.1271
0.1320
0.1298
A.6
CASES
In the CDP business, users influenced by the vidcos they have viewed in the past, which in turn inspires them to create. From the case study, we can see that ALM-MTA captures the causal relationship from video (treatment) Y (upload behavior) in multiple scenarios(see Fig
18

---

upload
treatment
Casel
eating
upload
treatment
Buyuguang
Case2
cartoon material
upload
treatment
Case3
outfit
upload
treatment
treatment
Case4
gesture dance
upload
treatment
Case5
role playing game
19
Figure 9: Causal Attribution from Historical Video Views to User Upload via ALM-MTA.


Reviews:
[{"review_id": "YFDYa480AU", "reviewer": "Reviewer_JKZ9", "summary": "The paper proposes using front-door identification to handle latent confounders in recommendation systems and innovative adversarial proxy mediator design. This method reduces variance in uplift estimation by conditioning on the proxy. It is claimed that the method is designed to be scalable with real-world performances. The paper presented business results on DAU, daily active creators, and an upload AUC of 0.907 (40% increase on sota). The paper performs an analysis on the result, presenting a propensity-stratified grouped AUUC protocol using Shapley value sampling, and the algorithm is able to combine multiple sophisticated techniques with ablation studies.", "questions": "- For comparison on other datasets and tasks, could more experiments be validated or provided to compare the proposed method against prior art? \n- Could the presentation be clearer on the figures? \n- Is the real-world data used made public for research comparisons?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper describes a novel application of the front-door identification causal framework to handle latent confounders in recommender systems. \n- The methods are experimented on a real-world online recommendation system, showing significant improvement. \n-The paper combines multiple complex methods together and shows ablation on each factor.", "weaknesses": "- It is less clear in the writing how the proposed methods align, compare, or improve upon methods in prior art \n- Compared with prior methods, other than Table 2, it could be useful to compare on more than one dataset or task, and perform experiments to show how the proposed methods work better and the specific reason why (e.g., due to front-door ID, adversarial mediator design, or scalability. \n- Presentation: Figure 5 and Figure 6 are a bit hard to read \n- it's a bit unclear whether the production or real-world data is made public for future work to compare to this paper", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "k4PHXmPM33", "reviewer": "Reviewer_uog4", "summary": "This paper presents a practically impactful and conceptually novel solution to causal multi-touch attribution in creator-driven recommendation systems. It addresses a critical gap where existing heuristic or back-door–based approaches fail under latent confounding, making attribution unreliable for optimizing creator ecosystems. The proposed ALM-MTA framework operationalizes a front-door causal design by introducing an adversarially learned proxy mediator, enabling identification of touchpoint-level causal effects even when the true mediator is unobserved. The method further integrates IPW debiasing and contrastive learning to ensure stability and overlap in large treatment spaces.\n\nThe contribution is notable for its theoretical grounding as well as its large-scale real-world impact, demonstrating measurable online gains on a 400M-DAU platform. This work stands out as one of the few that successfully bridges causal identification with production-level recommender optimization, making it relevant and valuable for both the causality and RS communities.", "questions": "Based on the the weaknesses, I have the following questions:\n\n1. Could the authors provide empirical or theoretical evidence that the front-door conditions truly hold in this CDP setting? Specifically, how do you justify the assumption that all causal influence of consumed touchpoints on uploads flows exclusively through the mediator $M$?\n\n2. How do the authors account for touchpoints that may directly trigger uploads without going through inspiration (such as the examples I established in weakness 2)? Would such direct causal paths violate front-door identification?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. A major strength lies in the novel design of an adversarially learned proxy-mediated front-door architecture. The paper introduces a proxy variable $Y'$ along with an adversarial learning mechanism to approximate the unobservable mediator required for front-door identification. This is a clever and original solution that operationalizes causal mediation where the true mediator is latent, helping to mitigate outcome leakage while preserving the causal pathway. The integration of IPW for debiasing and contrastive learning to maintain overlap in high-cardinality treatment spaces reflects a comprehensive and well-engineered approach to real-world constraints.\n\n2. The empirical evaluation is compelling. The model demonstrates consistent and substantial improvements over strong baselines in terms of AUC, log-loss, and gAUC. Particularly noteworthy is the inclusion of large-scale online A/B testing on a production system with 400M DAU and 30B training samples—showing statistically meaningful gains in creator activity, exposure efficiency, and platform health. Such deployment results significantly enhance the practical value and credibility of the work.", "weaknesses": "1. The use of the proxy variable $Y'$ as a surrogate for the mediator $M$ is insufficiently justified. The paper does not provide rigorous theoretical or empirical evidence establishing the validity of $Y'$ and it remains unclear whether $Y'$ meets the necessary proxy conditions for front-door identification. More importantly, the paper does not prove that $Y'$ does not introduce a new path to the outcome $Y$, which could bias the causal effect estimation.\n\n2. the applicability of the front-door criterion is assumed rather than validated. The key identification assumption—that all causal influence of touchpoints on uploads must operate exclusively through the latent mediator $M$ appears overly strong and may not hold in real-world CDP environments. In practice, certain types of touchpoints can directly trigger uploads without being reflected in the proxy-mediated “inspiration” pathway captured by $Y'$. For example, other creators get more rewards / exposure today can directly motivate uploads through platform incentives rather than creative inspiration, or some simple notifications (e.g., “Your followers are waiting—post an update now!”) can directly activate upload behavior and bypass the inspiration pathway altogether.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "0wQ0Mro9lt", "reviewer": "Reviewer_mDPn", "summary": "=\n\nThe paper targets multi-touch attribution (MTA) in creator/content ecosystems where there exist **unobserved confounders** and the standard back-door/front-door criteria are hard to apply directly. The authors propose **ALM-MTA**, a framework that combines front-door–style causal reasoning with **proxy variables, adversarial learning, IPW, and contrastive learning** to estimate causal contributions of different touchpoints. The key idea is: the true mediator in a front-door graph is not observed, so the model builds a *noisy proxy* for it and uses **adversarial training** to strip out components tied to the unobserved confounder, thereby recovering (approximately) the causal path from exposure/treatment to outcome. The method is evaluated with a causal-style ranking metric (gAUUC) and also with **online A/B tests**, which show business gains, making the work practically relevant.\n\n---\n\n### 2. Strengths\n\n#### 2.1 Conceptual and modeling contribution\n\n* The paper addresses a realistic variant of **front-door identification with an unobserved mediator**: in production systems we often can’t see the mediator that carries the effect, but we can see a *correlated, noisy signal*. Turning that into a front-door–like procedure via adversarial learning is clever and novel in the MTA context.\n* The adversarial branch is used to **force the main prediction network to drop information that helps predict the proxy** (which is assumed to be tied to the unobserved confounder). This is a reasonable deep-learning instantiation of “recovering the clean causal path.”\n\n#### 2.2 End-to-end, deployment-oriented design\n\n* The framework simultaneously considers **observed confounding** (handled via IPW), **unobserved confounding** (handled via adversarial proxy learning), **high-cardinality and sparse features** (mitigated via contrastive learning), and **training stability**. This kind of “full-stack” design is what real industrial MTA systems need, and is not always seen in academic MTA papers.\n* The paper clearly recognizes practical issues in creator ecosystems: heterogeneous touchpoints, weak overlap across users/items, and very large action spaces.\n\n#### 2.3 Evaluation and metrics\n\n* The paper proposes **gAUUC** to better align model ranking with *causal* uplift rather than predictive accuracy. This is more appropriate than plain AUC in a causal attribution setting.\n* Ablation studies show that removing the adversarial part or treating the problem as pure prediction leads to worse causal ranking, which supports the core claim.\n* **Online A/B** results (on DAU/WAU/creator WAU) make the contribution significantly stronger: the method is not only theoretically motivated but also deployable.\n\n---\n\n### 3. Weaknesses and Limitations\n\n#### 3.1 Complexity and reproducibility\n\n* The training pipeline optimizes multiple objectives at once (main prediction, proxy branch, adversarial loss, IPW-related parts, contrastive loss). This makes the system **heavy and hard to reproduce** for teams without mature ML/causal infra.\n* Because of this complexity, the method is somewhat **black-box**: it is not straightforward to tell which part of unobserved bias was actually removed.\n\n#### 3.2 Strong assumptions\n\n* The approach still relies—implicitly—on a **front-door–like identification story**: after conditioning on treatment and observed covariates, the unobserved confounder’s effect through the proxy/mediator is supposed to be “filterable.” In real platforms, this can be violated easily.\n* The whole adversarial cleaning step hinges on the **quality of the proxy**: if the available behavioral/log signal is only weakly correlated with the true mediator, the causal benefit may shrink.\n* The final attribution seems to assume **approximately additive contributions** across touchpoints, but in creator ecosystems **order and interaction effects** are common, so this assumption is somewhat fragile.\n\n#### 3.3 Metric self-referentiality\n\n* gAUUC, as defined, partially depends on model-based or constructed pseudo–ground truths. This can introduce a mild circularity (“we use a model to evaluate a model”). The paper acknowledges AUC is not ideal, but gAUUC is not yet a universally accepted gold standard.\n\n---\n\nSome suggestions\n\n1. **Make the causal graph explicit.** Present a minimal DAG, list the front-door conditions, and pinpoint which one is relaxed and how the proxy + adversarial block recovers it. This will help readers from the causal community.\n2. **Add proxy-quality sensitivity.** Vary the correlation between the proxy and the latent mediator, and show how performance (especially gAUUC and online KPIs) decays. This will make the method look more robust and honest.\n3. **Discuss non-additive / order effects.** Even a short section on how to extend to interaction-aware aggregation (e.g. attention over touch sequences) would ease concerns about the additivity assumption.\n4. **Provide a training recipe.** Since there are multiple losses, offer recommended weights, pretraining/warmup order, and common failure modes (overpowerful adversary, unstable IPW, etc.), so others can reimplement it.\n5. **Relate gAUUC to business KPIs.** Show which buckets / segments show the strongest monotonic relationship between gAUUC lifts and real online lifts, to justify the metric choice.", "questions": "see above", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "see above", "weaknesses": "see above", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "UWwZd3NRdN", "reviewer": "Reviewer_eh6j", "summary": "This paper abstracts the recommendation system into a multi-touch attribution problem. The authors argue that due to potential confounding factors, relying solely on backdoor adjustments is insufficient for reliable attribution. To achieve reliable attribution, the authors propose Adversarial Learning Mediator-based Multi-Touch Attribution (ALM-ATA). ALM-ATA leverages frontdoor principles and adversarial learning to refine outcome information and strengthen the causal path from treatment to outcome.", "questions": "See weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Using the front-door criterion to mitigate the impact of potential confounding factors\n2. Designing an Area Under the Uplift Curve evaluation scheme based on Shapley Value sampling, providing a new method for evaluating attribution quality on observational data\n3. Conducting experiments in real-world recommender systems", "weaknesses": "1. The authors need to further clarify their innovation. The authors claim to have solved the unobserved system-level confounding factors in the recommendation ecosystem and overcome a core limitation of previous attribution methods. However, in the field of debiased recommendation systems, several works have proposed solutions to potential confounding factors [1][2][3], including the application of the front-door criterion [2].\n2. How do the authors distinguish between covariates X and potential confounders W in practice, and how do they ensure that the model does not regard X as a confounder? From Figure 2, we can see that X can also be regarded as a confounder and is observable.\n3. The authors regard the observed results of Y as Y' and use it as a proxy variable for M to guide the generation of M. In my opinion, Y' is almost equivalent to Y, and there is a risk of data leakage. Although the authors claim to use adversarial learning to suppress data leakage and provide relevant experimental evidence in the \"Ablation Studies and Evaluation\" section, I still have concerns about this. I think the current experimental data is still the result of data leakage, but the degree of leakage has been suppressed.\n4. In Section 4, the authors did not describe the specific implementation details, but only provided a formal description. For example, the authors introduce adversarial learning but don't provide a detailed implementation. Similarly, the paper doesn't explain how the mediating variable M is generated. This raises concerns about whether ALM-ATA can be replicated.\n\nI am willing to increase my score if the author can address my doubts.\n\n[1] Z Huang, H Yuxuan, D Cheng, et al. Multi-Cause Deconfounding for Recommender Systems with Latent Confounders[J]. Knowledge-Based Systems, 2025. https://doi.org/10.1016/j.knosys.2025.114345\n[2] Xu S, Tan J, Heinecke S, et al. Deconfounded causal collaborative filtering[J]. ACM Transactions on Recommender Systems, 2023, 1(4): 1-25.\n[3] Xu H, Xu Y, Li C, et al. Causal structure representation learning of unobserved confounders in latent space for recommendation[J]. ACM Transactions on Information Systems, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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:

Decision: Accept (Poster)