LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
Authors: Yuchen Ma
Dennis Frauen
Jonas Schweisthal
Stefan Feuerriegel
Venue: NeurIPS 2025 poster ()
Keywords: Treatment Effect Estimation
LLMs
Text Confounding
Causal Inference
CATE
Doubly Robust
Primary area: deep_learning

Abstract: Estimating treatment effects is crucial for personalized decision-making in medicine, but this task faces unique challenges in clinical practice. At training time, models for estimating treatment effects are typically trained on well-structured medical datasets that contain detailed patient information. However, at inference time, predictions are often made using textual descriptions (e.g., descriptions with self-reported symptoms), which are incomplete representations of the original patient information. In this work, we make three contributions. (1) We show that the discrepancy between the data available during training time and inference time can lead to biased estimates of treatment effects. We formalize this issue as an \emph{inference time text confounding} problem, where confounders are fully observed during training time but only partially available through text at inference time. (2) To address this problem, we propose a novel framework for estimating treatment effects that explicitly accounts for inference time text confounding. Our framework leverages large language models (LLMs) together with a custom doubly robust learner to mitigate biases caused by the inference time text confounding. (3) Through a series of experiments, we demonstrate the effectiveness of our framework in real-world applications.

LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
Yuchen Ma, Dennis Frauen, Jonas Schweisthal & Stefan Feuerriegel
Munich Center for Machine Learning
LMU Munich yuchen vuchen.ma@@mu.cc mawlmu.dee
Abstract
Estimating treatment effects crucial for personalized decision-making in medicine, but this task faces unique challenges in clinical practice. At training time, models for estimating treatment effects are typically trained on well-structured medical datasets that contain detailed patient information. However, at inference time, predictions are often made using textual descriptions (e.g., descriptions with self-reported symptoms), which are incomplete representations of the original pa- tient information. this work, we make three contributions.
(1) We show that the discrepancy between the data available during training time and inference time can lead to biased estimates of treatment effects. We formalize this issue an inference time text confounding problem, where confounders ing time but only partially available through text at inference time. (2) To address this problem, propose novel framework for estimating treatment effects that explicitly accounts for inference time text confounding. Our framework leverages large language models (LLMs) together with custom doubly robust learner mitigate biases caused by the inference time text confounding. (3) Through a series of experiments, we demonstrate the effectiveness of our framework in real-world applications.
fully observed during train-
1
Introduction
Estimating the conditional average trealment effect (CATE) is crucial for personalized medicine growing number of machine learning methods have been developed for this purpose [e.g.. 6
14]
28 29 33
43 47 54 When estimating CATEs from observational data, it is necessary account for confounders L.e., variables that influence both treatment and outcome. If confounders are not properly adjusted for, the estimated treatment effects may not reflect the true causal effect and are thus biased 48
39
42
The standard setting for CATE estimation typically assumes that the same set confounders observed at training time and inference time [e.g.. 39 54). However, this assumption is often violated in real-world clinical practice. At training time, comprehensive information about confounders is typically available via well-curated medical datasets, such clinical registries or trial data, where structured patient records ensure that confounders are systematically recorded. At inference time, however, information about confounders may be only partially observed entirely missing due to differences in data collection methods, resource constraints, or changes in clinical workflow. In other words, there discrepancy: at inference time, the CATE often predicted from incomplete representations of the original patient information such as textual descriptions with self-reported symptoms.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Example (see Fi Consider a health insurance provider developing a medical chatbot to assist with treatment recommen- dations [55] [68] The chathot is trained on high-quality clinical datasets containing de- tailed patient histories, laboratory results, and physician assessments to ensure reli- able predictions of treatment effects. How- ever, at inference time, the chatbot relies primarily on free-text inputs from patients describing their symptoms, often without supporting diagnostic tests or clinical mea- surements. A similar challenge arises in emergency settings, such as during triage calls ambulance dispatches, where only verbal symptom descriptions or brief text messages are available.
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Figure 1: Discrepancy between training time and in- ference time. At training time, models for estimating treatment effects are typically trained on well-structured medical datasets that contain detailed patient informa- tion. However, at inference time (e.g., in telemedicine, remote healthcare consultations, or medical chatbots), predictions are often made using textual descriptions with self-reported symptoms. We formalize this dis- crepancy as inference time text confounding, where con- founders are fully observed during training time but only partially available through text at inference time.
The above introduces a discrepancy be- tween training time and test time: con- founders that were fully observed at train- ing time are only partially available through unstructured text at inference time, which can lead biased treatment effect estimates. We later formalize this discrepancy as inference time text confounding. In other words, if inference time text confounding is not properly addressed, any treatment effects estimated in the above medical example would be biased and could potentially lead to harmful treatment decisions. To address this, we further propose a novel framework compute unbiased estimates of the CATE, even in the presence of inference time text confounding.
Interestingly, the above setting involving inference time text confounding has not yet been studied. So far, standard methods for CATE estimation |c.g..39|55 (4)5 require identical sets with confounders at both training time and inference and are therefore
applicable. Further, these methods focus on confounders with structured data, not text data. Other works focus on settings with confounding due to text data (e.g.. [2] 10, 50, 61). yet these works again assume that confounders with text data available during training and are therefore also not applicable.
To fill the above gap, we propose a novel framework for unbiased CATE estimation in the presence of inference time text confounding. We call our framework TCA (short for text confounding adjustment). Thereby, we aim to bridge the gap between advanced treatment effect estimation methods for text data and real-worke medical constraints. Our TCA framework operates in three stages: 1 estimate nuisance functions and construct pscudo-outcome based on the true confounders; 2 generate surrogates of the text confounders by leveraging state-of-the-ar large language models (LLMs); and 3 perform doubly robust text-conditioned regression obtain valid and unbiased CATE estimates. Crucially, we use LLMs not as causal reasoners given their documented limitations in reliable inference 266) but as semantically rich text generator.
Intuitively, our TCA framework decouples treatment effect estimation (using true confounders) from inference time adjustments (using induced text confounders), which allows us to overcome the non-identifiability of CATE from text data alone. By conditioning on the generated text during training time, our framework learns to map the induced text confounders onto treatment effects, so that we can successfully remove the bias from having partially observed confounders at inference time. Further, our TCA is doubly robust and thus has favorable theoretical properties such as being robust to misspecification in the nuisance functions.
Overall, our main contributions are the following: (1) We formalize the problem of inference time text confounding, which common in real-world medical applications such as telemedicine, where full information about confounders is missing at inference time. (2) We develop a novel framework called TCA to adjust for inference time text confounding and provide unbiased treatment effect
'Code is available at tatttps://gitiin wtwpshwwww.omooccllmmmmeeemmaeee
2

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estimations. (3) We demonstrate the effectiveness and real-world applicability of our framework
across extensive experiments.
2 Related work
In the following, we focus on key literature streams relevant to our paper: (i) CATE estimation, (ii) treatment effect estimation from text, (iii) NLP for clinical decision support, and (iv) LLMs for causal inference. We provide an extended related work in Appendix A
CATE estimation:
Numerous methods have been proposed for estimating the CATE [e.g.. 28
29
33
39 47 54, 62). On the one hand, there are works that suggest specific model architectures to address the covariate shift between treated and control groups by learning balanced representations [28] 54
However, such specialized model architectures are not designed to handle unstructured data such as text. On the one hand, meta-learners offer a more flexible approach for constructing CATE estimators that can incorporate arbitrary machine learning models (e.g., neural networks) [ 19). Common meta-learners include the S-learner (39], T-leamer [39], and the DR-leamnrr (33). Nevertheless, it is
unclear how best to adapt meta-learners for complex settings such as those involving text data.
Of note, all of the above works operate in the standard CATE setting, where an identical set of confounders observed at both training and inference time. However, if the confounding variables different (c.g., if some confounders are only partially observed or hidden inference time), standard methods will yield biased estimates that no longer reflect the true treatment effect [48]. Motivated by this issue, we focus specific setting that is characterized by such discrepancy where confounders were fully observed at training time but are only partially available through unstructured text data inference time.
Treatment effect estimation with text data: A growing body of research has investigated causal inference with text data [e.g.. 2 10,
13
17
31
32
50 44
45
61
65
72
70
) Therein, text data
can have various roles: (a) text data can be trealment variable [e.g..
13
49
58
64
66) (b) text
data can be mediator [e.g.. 17 32 61 72 ); and (c) text data can be an outcome variable [e.g.. 16
38 57). In contrast to that, our setting considers text data as a confounder.
Several works have also explored (d) text data as a confounder [e.g.. 2 8 31 45
50
70 To 65
adjust for text confounding, recent methods typically attempt to mitigate the underly underlying confounding bias by viewing text features proxies for unobserved confounders TO 50 61
However, these works are orthogonal to setting. In the standard proxy-based based literature, the confounding variable is unobserved, but proxies are assumed to be available at both training and inference. In contrast, our setting introduces unique challenge due to inference time text confounding: the proxy (= text data) is unavailable during truining but are only available at inference time. Hence, proxy-based approaches are not applicable our setting. See the detailed comparison in Appendix D
NLP for clinical decision support: Natural language processing (NLP) has become a useful tool for clinical decision support, such as by enabling to extract information from unstructured clinical
texts [e.g.. 25
21 23 24). ). Early work focused on using NLP to process clinical notes and symptom deseriptions for some basic analysis and classification tasks, while more recent approaches leverage deep learning for learning the representations, particularly transformer- based
73
67
52
models like BERT and GPT [1 11 12 25 40). In similar vein, LLMs have shown promise in medical applications, such as clinical text summarization, diagnosis, and treatment recommendations
12 46
56
59
60).
However, most NLP models including LLMs designed for associative learning rather than causal reasoning [26] 27]. Hence, LLM fails addressing text confounding and would thus give biased estimates. This is unlike our TCA framework where we aim to give unbiased estimates.
LLMs for causal inference: Recent works have explored the capabilities of LLMs in causal reasoning across various causal tasks [e.g.. 18] 26 27 missing data for causal inference. However, recent works by [26) 27] demonstrate that LLMs fail to reliably reason about causality, which raises concerns given the need for reliable inferences, particularly in medicine [35], where flawed causal reasoning of L.M can lead harmful or even dangerous decisions.
37 69 7 For example. [12 leverages LLMs to impute
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In sum, the above works are orthogonal to our work. We are not aiming to use LLMs for causal reasoning, given their limitations in reliable inference. Instead. we leverage LLMs as an auxiliary tool: a semantically rich text generator in an intermediate step of our framework.
Differences to other settings: The main dif- ference between our setting and other settings lies in the availability and role of confounders during training and inference time, see Table !
Table I: Comparison of confounder availability across different settings. X: true confounder; T: induced text confounder.
(i) Comparison with the standard CATE esti- mation setting: Unlike the standard CATE set- tings ge.g.. 39 [4), where the same set of con- founders is observed at both training and infer- ence, our setting involves true confounder X that is observed during training but nor infer- ence (while some induced text confounder T is unobserved during training but observed at inference). (ii) Comparison with the proxy-based setting: In the proxy- setting TO 50 one views text T as a noisy proxy of true confounder X, which implies that the true confounders X should be assumed latent and are never directly observed, while the proxy T should always be observed. By contrast, in our setting, 'T' is not proxy but an induced variable from X, because T encodes partial information of the confounders. T' is unobserved during training but fully observed during inference, whereas X is observed during training but unobserved during inference, which is opposite to the proxy setting.
Training time
Inference time
Setting
X
T
x
T
Standard CATE setting
/
x
/
Proxy- based setting
x
4
x
/
Our novel setting X: Not available; v: Available.
x
x
We focus on CATE estimation for clinical decision support where confounders are only partially available through text at inference time. To the best of our knowledge, we are the first to formalize the underlying issue as inference time text confounding and propose a novel framework tailored give unbiased CATE estimation.
Research gap:
3 Problem setup
Setting: We consider treatment A E A 13 (e.g., an anti-hypertension drug) and the outcome of interest Y ) C I. (e.g., cardiovascular events), for which we want to estimate the treatment effect. In our setting, we further consider the following discrepancy between training time and inference time:
Training time:
Here, we observe confounders X X' C Rdz, For example, this could be well- curated information from electronic health records such as blood pressure readings, cholesterol levels, and kidney function tests. During training, thus have access to observational dataset Dx = (zz, 04 3)) sampled i..dd from the unknown joint distribution P',,T,,,,
Inference time: Here, we do not observe the confounders X. Rather, observe some textual representation (e.g., a self-description of symptoms such as "I feel dizzy" or "I have headaches") but without access laboratory results prior prescriptions. Formally, we observe some induced text confounders T, while the true confounders X are unavailable. The induced text confounders T T Rd are generated by X, which are unobserved during training but observed at inference time. The test dataset is DD= (li, ai, yi ) and also sampled i.i.d. from the same joint distribution P(X,T, Y.).
The underlying causal graph for our setting is shown in Fig. We refer to the above discrepancy as inference time text confounding.
Clinical relevance: Our setting is highly relevant in medical practice. One application telemedicine
[99 where CATE models are trained on well-curated medical datasets (e.g., trial data) containing comprehensive confounder information (where X includes patient characteristies and detailed di- agnostic features, different risk factors, e.g., age, gender, prior diseases). Hence, at training time, detailed information about confounders is available.
contrast, at inference time, detailed patient information is unavailable, simply because diagnostic facilities are not available in telemedicine. Hence, at inference time, the CATE model can only be applied to symptom descriptions from patients (where T' provides partial information about the patient characteristics and diagnostic features in X).

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Notation: We use capital letters to denote random variables and small letters for their realizations from corresponding spaces. We denote the propensity score by ma (2)) = Pr(A a X =)), which is the treatment assignment mechanism in the observational data. We denote the response surfaces via (x) F|y X x; A a). Importantly, we later use superscripts to distinguish between training/inference: we use Taa M4 to refer to nuisance functions related to the true confounder X and Tg. v for nuisance functions related to the induced text confounder T.
Potential outcomes framework: We build upon Neyman- potential outcomes frame- work [51]. Hence, (()) E ) denotes the poten- tial outcome for treatment intervention A a. We have two potential outcomes for each in- dividual: (1)) the treated (i.e., = 1), and (() if not treated (i.e., A = 0). How- ever, due to the fundamental problem of causal inference (22), only one of the potential out- comes is observed. Hence, Y = (1) + - (1 (0). The conditional average treat- ment effect (CATE) with respect to confounder X is defined as T(x) (1) (0) X
(a)
(6)
Figure 2: Causal graph for inference time text confounding, (a) At training time, we have access to the true confounders X but the induced test confounders T are unobserved. (b) At inference time, the induced text confounders 7 are observed, while the true confounders X are unavailable.
z). which is the expected treatment effect for an individual with covariate value X
We follow previous literature [e.g.. 6 33
54 62 to make the following standard assumptions to ensure the identifiablity of the CATE from observational data.
Assumption 3.1. (i) Consistency: if A then Y (a); (ii) Unconfoundedness: Y Y()), Y(1) T
A X: (iii) Overlap: Pr < n. (x) < 1.
Of note, the above assumptions are standard in causality 48 Consistency ensures that the observed outcome Y aligns with the potential outcome Y (a) under the assigned treatment A a. Unconfound- edness implies there no unobserved confounders, i..., all factors influencing both the treatment and the outcome are captured in the observed covariates X. Overlap guarantees that the treatment assignment is non-deterministic, i.c., that every individual has non-zero probability of receiving each treatment option.
In practice, it is natural to view the text as a noisy 'measurement" of the true confounder. We thus formalize the induced text confounder generated from X.
Assumption 3.2 (Text generation mechanism). There exists a measurable function h A' x E + T, and random variable e with support E, such that the induced text confounder T is generated from the true confounder X E X' as T = h(X, e), where the noise variable satisfies e 1L Y (a) X, i.c. the noise is independent of the potential outcomes conditional on the true confounder X.
For ease of readability, we use different colors to distinguish the CATE with respect to true confounder X, xx7")x) and the CATE with respect to induced text confounder T, (t). In the standard setting, -=(x) is often of interest. However, here, our target (L).. At inference time, we focus on estimating (t) J (1) (0) T t). which is the expected treatment effect for an individual with covariate value T
Objective:
4 Our novel TCA framework to adjust for inference time text confounding
Overview: In this section, we introduce our text confounding adjustment (TCA) framework for addressing inference time text confounding. We first discuss the challenges of CATE estimation in inference time text confounding (Sec. [.I). where we demonstrate the limitation of naïve methods due to estimation bias and thereby motivate the need for a novel method to achieve unbiased estimation. We then present our text generation (Sec. and doubly-robust CATE estimation procedure (Sec. 4.3 Our full TCA framework is given in Sec. 4.4
4.1 Adjusting for inference time text confounding
Why is CATE estimation non-trivial in our setting? Estimating the CATE in inference time text confounding is very challenging. Recall that the CATE with respect to true confounder X, (x),
5

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and the CATE with respect to induced text confounder T. (t). are different. At training time, under Assumption 3.1 the CATE T" (z) can be directly identified from observational data Dx (1i, ds: V1)==1 as - (x) H (z). However, the larget estimand at inference time, 7 71), is
non-identifiable from observational test data Dr = (tj, aj 83) j=1 through T alone.
How do naive baselines work? A naive baseline would attempt to directly estimate (1) from Dr Assuming that T would be observed during training. a model trained to learn the response surface with respect to n that gives E A = a tl. Here, naive baseline directly estimates T "navv (t) = IE[Y = 1, = E -
Why do naive baselines fail? A naïve method estimates Thavve (t) which is not equal to the true CATE (t). The induced text confounder generated from X often does not capture all the necessary confounding information in X for potential outcomes. As result, there exists confounding information in the residual confounders X T that affects both and Y, which are unobserved in Dr. Due to the residual confounding, we have E[Y A == a, 7 1) o (Y T = 4.. Below, we state the pointwise confounding bias of the naïve baseline following (5)
Lemma 4.1 (Pointwise confounding bias of the naïve baseline). For any t E 'T, under Assumption 3. the naive baseline estimating T Fiavve()) (t) has pointwise confounding bias with respect to the true CATE T (f), given by
bias(t) naivo (t) - (t)
(F(uf(X) A A= 1, - [[F((X) - IT=
(I)
-(E(16(X) 1A 0, -[(p((x -T= =]]
Proof. The proof is in the Appendix B.1
In the context of inference time text confounding, when T does not capture all the necessary confounding information in X for potential outcome, the naive estimator is necessarily biased, as
E (x) | A = a, a, t + (x =
(2)
Remark 4.2 (Non- (Non-zero zero bias of the naïve estimator). The bias of the naive estimator remains non-zero under Assumption 3.1 and (O)) (1)) / A |
How to properly adjust for inference time text confounding? To address the challenges due to inference time text confounding, we reformulate the target estimand r' (t). Our solution leverages the fact that we have access to the true confounder X in the training data circumvent the problems arising from the test data with T alone.
Lemma 4.3 (Identifiablity of rt)). For any t E T, under Assumption 3.2 rt( can be identified through 7 (xx) via
EE) (X) T=
Proof. Proof is in the Appendix B.2
Lemma 4.3 gives us unbiased estimation of 71(1) through (x)). Given the identifiability of 7 (z). we can estimate (I) via
(()) E (x)((x) 1 (X) T =
(4)
Importantly, the above reformulation motivates our approach: the reformulation essentially addresses the estimation challenge by first learning the ground-truth response surfaces μ.""" from Dx and then conditioning on the inference time text confounder T. Eq. alllow us to construct tailored training procedure where we first fit response surfaces 1*"" using Dx. and then perform text-conditioned CATE regression by training an auxiliary model to estimate E (X) T t). this way, at training time, the model learns the function mapping from text confounder T to the true CATE ((). Once finishing training the model, at test time when X is unobservable, the model still ensures unbiased CATE estimation in the presence of inference time text confounding.

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4.2 Text-based surrogate confounder
The above analysis assumes access the induced text confounder T during training. However, in the practical setting, we only have access to the confounder X. Hence, to be able to nevertheless leverage our reformulation in Lemma 4.3 we construct a text-based surrogate confounder T g(X) through an L.LM-base text generation procedure, where g : X' >> maps structured features text space. Importantly, by using g this way, we map confounder X to the induced text confounder T naturally follows the text generation mechanism in Assumption 3.2 as the only input to the LLM is the X. thus the key confounding information in X is carried over into T with some random noise from the LLM.
Note that our method does not require the induced text confounder T to contain all the necessary information from the true confounder X for potential outcomes to ensure unbiased estimation of (1). Instead, we allow 7 to preserve only partial confounding information in X, which is essentially consistent with real-world scenarios, where information about true confounding at inference time can be partially observed or missing. For instance, if X includes diagnostic measurements such as heart rate, T might be a self-reported description of the symptoms such as "My heart is racing".
We thus construct our training data Dxx = (zi; Li ai 9:) ==) The generation details can be found in Sec. and Appendix C The surrogate T enables us to adapt the key identity from Sec. 4.1 that
(t) can be computed by E (X) 15 (X) = z.. This text generation step transforms our
theoretical identifiability result into practical framework 2 In Sec. 6 we empirically validate that including this step helps estimate 7' (t) more effectively than methods ignoring residual confounding.
4.3 Doubly-robust CATE estimation with text confounders
Here, we adapt a state-of-ie ofate-of-the-ar CATE meta-learner for our framework. Specifically, we leverage a doubly-robust (DR) learner (33) for estimating CATE 7²(t) due to the favorable theoretical property of being double robust. This step has two sub-steps: (i) Nuisance functions estimation using the true confounder X and pseudo-outcomes construction; and (ii) text-conditioned regression with pseudo-outcomes.
Estimating nuisance functions and pseudo-outcomes: First, we estimate the response surface o (2) and the propensity score n" (x) using training data Dx. Let (x) = (Ar(x) ( (=)),)())))) denote the estimated nuisance functions, where P% (x) is the estimated response surface for treatment A = a" (xx) is the estimated propensity score. Following (5), these estimates enable the construction of a doubly-robust pscudo-outcome for each observation sample via
A,
1- A jr (x:)) 1- (I:)
Y; = (x;) i (cx:
(Y:
(Y 10 (r:))
(5)
A (x:)
The first term estimates the CATE conditioned on X, while the remaining terms apply inverse propensity weighting to correct for potential biases in the response surface estimates.
To estimate (1( using the auxiliary dataset Dxx from Sec. 4.2 map the text T' to the text embedding ((T') using a pretrained LLM. We then train regression model fo T + IR to predict the pseudo- peudo-outcome Y from text embeddings ((T) by minimizing the loss
Text-conditioned regression:
(()) = Σ fo (o(E))) 2 111011
(6)
where is a regularization parameter. At inference time, the final CATE estimate given by (t( = fo((t))) which maps text inputs to CATE estimates.
Corollary 4.4 (Double robustness property of the estimator). The estimator satisfies the double robustness property by construction: (t) converges to the true 7 (t) if either (i) the response surface estimates Sh are consistent, or (ii) the propensity score estimate 4t consistent. This ensures validity even under potential model misspecification.
Modem LLMs' ability to transfer structured data into semantically rich text provides a principled approxi- mation. While discrepancies of distribution between T and the hypothetical I' may exist, can be mitigated by some domain adaptation methods. For example, if we have access the example T., we adapt I' accordingly.
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We refer to the Appendix B.3 for the detailed proof.
4.4 TCA framework
Training: Our framework text confounding adjustment (TCA) operates through three stages. Stage Q:: Estimating muisance functions and pseudo-outcome. We first estimate the nuisance functions based on Dx and construct doubly-robust pseudo-outcome, Stage ④: Generating the text-based surrogate confounder. We generate text surrogates T through an LLM-based mapping, which allows to link the true confounder to the induced text confounder. Stage 3: Doubly-robust rext-conditione regression. We perform a doubly- robust CATE estimation, where train the regression model with pseudo-outcome conditioned on text to estimate CATE The full TCA is shown in Algorithm |
Inference: At inference time, we are given a new text sample from DT and compute CATE via (1()) = (o(())).
Therein, we address inference time text confounding by reformulating the target estimand with the true confounder X via Eq. f which establishes the theoretical foundation for our method in CATE estimation: we decouple the treatment effect estimation (using X) from the test-time adaptation (using T), which allows us to overcome the non-identifiability of (1) from Dr alone. By conditioning
on the generated T during training, our framework allows us to learn a mapping of the induced text confounder onto the treatment effects.
TCA for CATE estimation with in- ference time text confounding.
Algorithm 1:
5 Implementation details
Input: Dx = {(1r;, ai, M:) )= test data
Given structured clinical confounders X,
Dr = (t,, o, y= text generator g. pretrained
generate text confounders 7" via the OpenAl
encoder Φb regularization
API [ The generated narratives are approx-
Training:
imately 150-200 tokens long. We obtain rep- resentations of 7 using pretrained BERT [11].
D Stage !
Estimate nuisance functions on Dx:
Token embeddings from the final transformer layer undergo mean pooling, yielding fixed-
* Fit (z(x) +
arg min, Ein (yi ua ( 2 Va (0. 13
dimensional representations 6(t) E 768
Fit A (x) +- arg max, Et= [a, log (2,) +
Training time and further implementation de-
tails are in Appendix C
(1 - as) log (1 - x<x.))
for to do
6 Experiment
Construct doubly robust pscudo-outcomes
< (x.) (x;)) 2: (3/-
6.1 Setup
( (2, (v+ - 145 (z.))
Datasets: We use the following datasets from medical practice for benchmarking: (i) The International Stroke Trial (IST) (53) is one of the largest randomized controlled trials in acute stroke treatment. The dataset com-
10 >> Stage 2
11
for to r do
Generate text surrogates i, g(xxa
12
13
Get text embeddings 2; +- (o(s)
14
D Stage 3
prises 19, 435 patients. (ii) MIMIC-III (30) is a large, single-center database comprising information relating to patients admitted to critical care units large tertiary care hos- pital. MIMIC-III contains 38, 597 distinct adult patients. We adhere to the terms and conditions governing the use of the MIMIC dataset (in particular, our analysis is HIPAA compliant). Details are in Appendix E
15 Train CATE learner fo via
fo arg ming
Σm_ (ūr (ol-fo(2))) (
16 Inference:
Predict CATE given new text input ty E Dr via 17
*(tt) + fo (o(tt)
18 Output: CATE estimator ('(1)
Due to the fundamental problem of causal inference, the counterfactual outcomes never observed in real-world data. We thus follow prior literature (, 39 (54) and benchmark our model using semi-synthetic datasets. Details of datasets are in Appendix
Baselines: Due to the novelty of our setting, there are no existing methods tailored for this inference time text confounding setting. Hence, compare our method against the naïve text-based estima-
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Figure 3: Performance of CATE estimation under varying confounder strengths and prompt strategies across datasets.
1N
1.27
112
211-
Stroog
String
(a) Results for CATE estimation under varying con- founder strengths. (a): IST dataset. (b): MIMIC-III dataset.
(b) Results for CATE estimation under varying prompt
strategies. (a): IST dataset. (b): MIMIC-III dataset.
Table 2: Performance of CATE estimation evaluated by PEHE across different demographic subgroups for each dataset. Reported: mean + standard deviation.
IST
MIMIC-III
IST
MIMIC-III
Ga
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Gu
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0.197 -014
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0.283
TBE- TBE-T-leamer
0.195 s.u
0.197 6.s
0.279 co
0.28 430
TBE-T-learee -BE-TA
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0,195 k:3)
0.282 800
0.279 so0
TEE TARNet
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0.192 k3
0.193 ozz
0.275 Loaz
0.277 .aao
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TBE-CFR
51
0.191
0.193
0.274 Loc
0.275
TBE-CFR
0,195
0.19]
0.275
0.273
(Ouns)
oz
1.00
0.178
Jower better (best in hold).
Tower better (best holl).
training standard CATE learners directly on the text features ((T). We compare with the TBE-THearee [39]; TBE-S-learee [39]; TBE-TARNet (54): TBE-CFRNet (54). Note that
tors (BBE):
use the same T and of (TT) for both our method and the baselines to ensure a fair comparison. We also used equivalent syper-paramette
across all baselines, thus ensuring that basclines can have the same number of hidden layers and units. (See implementation details in Appendix CC)
Evaluation metrics: We report the precision of estimating the heterogeneous effects (PEHE) criterion 20) to evaluate the performance of our framework estimating the CATE. We reported results for 5 runs each.
6.2 Results
Benchmarking results for CATE estimation: We report the performance CATE estimation in Table Our TCA consis- tently outperforms the baseline methods across both datasets and groups. The improvement substantial, as the error rates of our TCA are approximately 40% and 35% lower than the best bascline methods on IST and MIMIC-III, respectively. This demonstrates the effectiveness of our framework mit- igating bias from residual confounding at inference time.
Table 3: Benchmarking results for CATE estimation evaluated using PEHE on the IST and MIMIC-III datasets. Reported: mean + stan- dard deviation.
1ST
MIMIC-III
TBE-S-Jeance
39
0.198 2010
0.283 20:2
TBE-T Tearmer
3V
0.196 0.0
0.280 km
TBE-TARNEL
0.275
Additional studies: We further conduct additional studies to gain deeper understanding of our framework.
TBE-CI [54]
0.192 0.0
0.274 4642
TCA (Ours)
0.116
0.179
Lower better (best bald).
Varying confounder strengths: We evaluate the perfor- mance of TCA in estimating CATE under different confound-
ing strengths by varying the influence of true confounders X to assess the impact of residual confounding on estimation accuracy. Results are in Fig. 3al It demonstrates the effectiveness and robustness of our framework under varying confounder strength.
Varying prompt strategies: We further analyze the impact of prompt engineering on LLM- based generation by evaluating three different prompt strategies: (i) Factual prompts (Pi) are a basic approach using a fixed template. Here, the prompts directly convert the patient information using fixed structure (e.g., "Transfer this patient information into paragraph of text.") ") (ii) Nar- rative prompts (Pz) allow to capture rich context. Here, the prompts capture detailed symp- tom experiences but exclude diagnostics (e.g., "Arite datailed clinical narrative patient with these features.") ") (iii) Symptom-foctt focused prompt (Pa) (advanced): Patient-centric symptom descrip- tions (c.g., "Box this patient just not diagnostic zessurenente available. vould patient

---

describe his/her feoling by text?"). Detailed examples of these prompts are provided in Appendix D.4 Results are in Fig Evidently, we see our method outperform baselines across all prompt strategies. confirming the effectiveness of TCA.
Varying LLMs: We further analyze the impact of differ- ent LLMs. We show the results for benchmarking CATE estimation with PEHE on IST and MIMIC-III datasets using GPT-3.5 Turbo in T It shows that our method still per- forms better than the baselines regardless of the LLMs used for generating text.
Table 4: Results of CATE estima- tion benchmarking evaluated with PEHE on the IST and MIMIC-III datasets under varying LLMs. Re- ported: mean + standard deviation.
1ST
MIMIC-III
Our framework leverages LLM-derived text to capture an induced text confounder during training. Hence, we acknowledge risks from LLM use, such biases or data misrepresentation. We thus conduct experiments on subgroups in the datasets. (i) We split the datasets by gender into two groups Gp and GM with females and males, respec- tively. (ii) We split the datasets by age into two groups Gy and Go age before above 45, respectively. Results are shown in Table 2 We observe minimal performance variation across subgroups, suggesting that the LLMs do not introduce significant cabbrroup-specifie bias. Our method consistently outperforms baselines across different subpopulations.
Further insights:
TBE-S learner
39
0.224 gam
0.314 400
TBE-T-leamer
39
0.221
0.310
TRE-TARNet
0.218 gm0
TBE-CFR [54)
0.216 om
0.299 20e
TCA (Ours)
0.141 0.0
Lower (best bold).
Additional comparison with DR-learner: Our TCA differs substantially from the standard DR- Learner. A simple off-the-shelf combination of our text-generation step with existing DR-learners would be biased. We additionally conduct experiments where combine our text- text-generation pipeline with standard DR learner (33) (referred to as TBE-DR), Importantly, we made the experiment fair: we used the same setup for the LLMs, etc. We report PEHE scores on two datasets, as shown in 5
From the results, a large margin. This baseline uses the same underlying estima- tion technique ours but lacks our framework for confounding adjustment. These results highlight that the core innovation lies in our confounding adjustment framework, not in the choice of DR itself. In general, while our approach leverages the doubly robust estimation benefit from the doubly robust properties, it could also be easily adapted to use other meta-learners in the final step. The novelty lies in how we adjust for the text time confounding.
TCA outperforms TBE-DR by
Table 5: Additional compari- son with DR-leamer. Reported: mean + standard deviation.
1ST
MIMIC-III
TBE DR 33
0.187
0,266 go0:
TCA (Ours)
0.116
0.179
better (best bold).
Our paper highlights the challenge of inference time text confounding for treatment effect estimation, where confounders that are fully observed during training only partially observable through text at inference. This is common in any application of personalized medicine involving text input during clinical deployments, such as chatbots medical LLMs for question answering. Our results show that our TCA framework can effectively yield reliable treatment effect estimates for personalized medicine, even when only limited textual descriptions are available at inference time.
Conclusion:
7 Acknowledgments
This work has been supported by the German Federal Ministry of Education and Research (Grant: 011524082).
10

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Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality the research, declaration is not required.
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A Extended related work
A.1
Conditional average treatment effect (CATE)
Estimating the CATE has received a lot of attention in the machine learning literature (c.g..
54 62) A prominent approach to CATE estimation involves meta- lettaee I.e., feexille strategies that decouple treatment effect estimation from the choice of
28
29 39
33
41
42
43
47
base machine learning model. First systematized by (39), these methods have since been extended
through theoretical analysis [34]]]]] 47) and improved pseudo-outcome constructions 33
Existing CATE meta-learnrrs can be categorized into: (a) one-step (plug-in) learners (indirect meta- learners) that output two regression functions from the observational data and then compute CATE as the difference in the potential outcomes (this the strategy underlying the S- and T-learners); direct estimators). These learners first
and (b) two-step learners (direct poottallarners/mmlii-ttttte compute nuisance functions to build pseudo-outcome. In the second step, they obtain the CATE directly by regressing the input covariates on the pseudo pseudo-outcome. (Note that pseudo-outcomes are not potential outcomes). In terms of (b), existing methods fall largely into three broad classes: regression adjustment (RA), propensity weighting (PW), or doubly robust (DR) strategies.
The RA-learner uses the regression-adjusted pseudo-outcome in the second step, i.e.,
YRA.A = A (Y - -o((X) + (1 - A) (X)(X) Y)
The PW-leaner is inspired by inverse propensity- (IPW) estimators, which is associated with pseudo-outcome, i.c.,
YPA. = (14)
A 1- A (X)) (=(
- x(x) )
(8)
1 1
Doubly robust (DR) learners combine elements of RA and PW mitigate their individual limitations. is an extensions of augmented inverse probability weighting (AlPW) by constructing pseudo- outcomes using both propensity scores and outcome models. The pseudo-outcome is defined as:
A A(X)
A1
[
A #(X)
(1(X) (1 (1- (-x))
1 1- A
Ao Ro(x))
YDR=
Y
(9)
It can also be written in the residual form
YDR م (X) (X) + A (Y - (x)) (1- A) (Y (xo)
(10)
+(X)
+X))
As the latter is based on the doubly-robust AIPW estimator, it is hence unbiased if either propensity score or outcome regressions are correctly specified 33
A.2
Causal inference with text data
Causal inference with text data has emerged an important research direction (e.g. 61 31 50 45
72
17
70
10 13 21), with text variables serving multiple roles in causal frameworks.
Prior research has considered text to have various causal roles, with text serving as treatment, mediator. outcome, or confounder. For example, previous works view text as treatment including [e.g.,58 66
64 49
13]: works view text as mediator includingle.g. 72 61 17 32 ); and works
view text as outcome inc mncludigg [e.g.. [6. 57 38). There are many works that have explored text as a confounder [e.g.,31 31
50 45 70, 2). [31] surveyed methods leveraging text to remove confounding
and challenges. Some work attempts to mitigate confounding bias by viewing text features act as
proxies for unobserved confounders [6]] [5 10 2
A.3 NLP and language models in medical decision-making
Natural language processing (NLP) has emerged as an important tool healthcare, enabling the extraction of meaningful information from unstructured clinical texts (e.g..9)
25
73
67
52
21
23 (4)). Early work focused on using NI.P to process clinical notes and symptom descriptions for some basic analysis and classification tasks, while more recent approaches leverage deep learning for
22

---

learning the representations, particularly trunsormer-bassee models like BERT and GPT II 25
12 40].
Work in this area has focused learning the representations; for instance, [25] improved on previous clinical text processing methods and proposed a pretrained model on clinical notes to learn better representation for prediction. Other work used L.LMs to impute the missing data clinical notes 12].
Large language models (LLMs) have shown promise in medical applications. such as clinical text summarization, diagnosis, and treatment recommendations (59
60 12]. For example, (36] introduce multi-agent framework that leverages LLMs to emulate the hierarchical diagnosis procedures, targeted for symptom classification and risk stratification tasks. However, unstructured text data often lacks explicit information about key confounders, which is critical for accurate causal inference. most NLP models, including LLMs, are designed for associative learning rather than causal reasoning (27 (26). Their use causal inference, particularly addressing text confounding, remains underexplored.
46
56
A.4
Counterfactual predictions under runtime confounding
Our setting is also related [5], which refers scenario where historical data contains all the relevant information needed for decision-making. Only subset of covariates V C X' is available to use runtime. The unobserved set X V should include all the hidden confounders the runtime, denoted as Z. They show this can induce considerable bias in the resulting prediction model when the discarded features are significant confounders. However, this setting is different from ours. They require the set V at runtime known and fixed. This is strong and unrealistic assumption in our setting. Our setting allows to an arbitrary subset of X. Moreover, their setting cannot handle the modality of data, which involves structured clinical tabular data at training time and only self-reported description- based text data at inference time.
23

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B Proof
B.1
Pointwise confounding bias of the naïve baseline
Lemma B.1 (Pointwise confounding bias of the naive baseline). Under inference time text con- founding, assume that Assumption 3. holds. Then, for any t T. the naive estimator defined as naivve (t) = E[ A = 1,T"= = B A = 0,T* = t has pointwise bias with respect to the true conditional average treatment effect (CATE) r² (t). given by
bias(t) nniveo (t) - (1)
= (x)
11=1,7- - -1)
(11)
(E( x))
A = -
where (x) = | 2 a.
Proof. We give the proof by following
By the consistency (Assumption 3.1 (i), we have
B[Y A a, T = - (a) A a, t].
(12)
By the unconfoundedness (Assumption 3.1 ii)) and induced text generation mechanism (the inde- pendence of the noise e, i... e 1l (a) obtain
X), and by applying the law of iterated expectations, we
(a) = a, T = 1) = (()
= - - 1).
(13)
Hence, the naïve estimator can be written as
navee (f) E (x) A A = $ B[pσ(}) A -
(14)
The target CATE given 7 is defined by
t' (t) = (1) Y T' =
(15)
Subtracting the true CATE from the naïve estimator yields the bias
bias(t) = Thaive (t) - (t)
= (z) A = -
:)) - E[u((x) - -16(x) T
(x)
-4=1, 1 - | = =1]
- -(B(p5(0) 0,7 -
T r d
(16) D
Remark B.2 (Non-zen) zero bias of the naive estimator). The bias of the naïve estimator remains non-zero under Assumption 3.1 and Y()). (1)) y |
In the context of inference time text confounding, when T does not capture all the necessary confounding information in X for potential outcome, the naive estimator is necessarily biased, as
E. 1 (X) A a, = 1] (
(17)
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B.2
Identifiablity of (t) by adjusting for inference time text confounding
Lemma B.3 (Identifiablity of (t)). For any t T, under Assumption 3.2 T'(t) can be identified
through T" (2) via
-' (t) E [X) t].
(18)
Proof. By definition, the CATE with respect to the induced text confounder is
(4) (1) - Y(0) - 1].
(19)
Applying the law of iterated expectations, we can condition on the true confounder X:
71))) (1E (0) - T = =].
(20)
Under Assumption 3.2 conditional X, the variable T does not provide any additional information about the potential outcomes, we have
(1) - (() X,T = = E - Y()) x] -" (x).
(21)
Thus, obtain
7(t) (())=B(ro))T==
(22)
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B.3 Double robustness property of the estimator
Corollary B.4 (Double robustness property of the estimator). The estimator satisfies the double robustness property by construction: (t) converges to the true T (t) if either (i) the response surface estimates Pag are consistent, or (ii) the propensity score estimate a" consistent. This ensures validity even under potential model misspecification.
Here, give the proof of the Corollary B.4 which states that our estimator satisfies the double robustness property. Formally, we have the response functions defined as (z) = E[ X =
A a), and T" (x) P(A = 1| X x) as the true propensity score. Then, the true conditional
average treatment effect (CATE) is
T 7(x) = mf(x) - 1z()))
(23)
Our doubly robust pseudo-outcome is given as
Y = (x) (x)
(x) (Y (y- (f(())
A
1 1- +xxx ( (6(x))
1-A
(24)
where (z) and A" (I) are estimators of (z) and x" (z), respectively.
We now show that, under the Assumption 3.1 either
1. the outcome models are correctly specified, i... (1 = (z) for (0,1), or
2. the propensity score model is correctly specified, i.e., (x) 1² (2),
it follows that
E
Y X = =
(25)
Proof. We give the proof following 33
We treat the two cases separately.
Case 1: Correct outcome models.
Assume that P (I) p (z) for E {0, 1}. Then, the pseudo-outcome reduces to
Y (x) (x)
A
Yy m((x))
1- A
+=(x)) (y 16(x)).
(26)
オ +अ)x)
Taking the expectation conditional on X = x gives
E x
μ (x) 15(x))
A
(Y (y-) | x = z)
+ E
(27)
(x) (x- -(((())) x =
E
We have
Y - (x) X = x, A= = =0 and
E - -+1x) / X = x, A= =,
(28)
and, as both expectation terms vanish, yielding
E Y
x
(z(x) - (x))
(29)
Case 2: Correct propensity score model. Now, assume that *=(x)) = nx" (ar,, while allowing for
misspecification the outcome models. We define the errors
(z) P. (z) - (z(),, a 10.
(30)
so that (x) (x) + (x). Then, the pseudo-outcome can be written
Ý (x) + 51 (z)) [mō(x)) 50(z))
क. (z) (Y - 1f(2x) 8, (1:)
A
(31)
Y - 1 (2) - 60(x))
- n (x)
26

---

Taking the conditional expectation given X z, we have
E
Y
x
=
x|
= (ī(x)) - - (5(x))
+ 51(x) - (6)())
+ ग (ス)
1
E
A
Y un(x)) - -6((z)) 1 1x= =
(32)
1
EE (1 () m5(()) - 50(x)) X - =1
1 T (x)
Noting that IE[Y |X = x, A = a| = (z), it follows that
E [[A(Y (Y m=(()) X = z =0 and E (1 - (0(x-1((0)) 1τ(x)) X xx=x =0) =0. (33)
Furthermore, by linearity,
I [Ai = z] (x)
( A))(()))())) / x x==
x]] = -0((x) (1 - -x(())) (34)
Thus, we have
T (xx) B B[A(s Y - (x(x) - -d((z)) |x = = = -d1(z))
(35)
and
(x) B a) rz(()) 50(0)) X 2/ =oo(z))
(36)
Substituting these back, we obtain
E - [f]X==]= p((()) 1z(z)) + (1) (1)))
(x] (1)
(37)
(x)
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C Implementation details
Text generation: Given structured clinical confounders X, generate the induced text confounder T using GPT-4o mini through the OpenAl API. For each patient, we construct prompts by templating the key features of X into natural language constraints. Generated texts are approximately 150 - 200 tokens.
Text embedding: We convert generated text into dense vectors using pretrained BERT (bert- base-uncased) from HuggingFace Transformers. For each narrative, compute token embeddings from the final transformer layer and apply mean pooling across tokens, yielding fixed-dimensional representations (t:) IR 768.
Model architecture and training: Propensity scores A" (*)x) are estimated via logistic regression The CATE predictor is 3-layer MLP with ReLU activation, batch normalization, and 0.3 dropout. We use Adam optimizer with learning rate 5e 5. Models are trained for 100 epochs on IST and 150 on MIMIC-III with batch size of 512. We apply label smoothing a and gradient clipping (max norm=1.0)
Reported time: Experiments were carried on 2 GPUs (NVIDIA A A100-PCIE-40GB) with Intel Xeon Silver 4316 CPUs. Constructing each dataset took approximately 50 hours. Training our TCA takes around 10 minutes on average, comparable the baseline training times.
Baselines: We follow the implementation from https: / giub com/ Ali aliciaCurth/ CoA.Gurrt/CAEEeetss.. CATENets/ cree/main| for most of the CATE estimators, including S-Net [39]. T-Net [39]. TARNet [54).
CHRNet (54). Regarding the proxy-based methods 66] 2 we follow the implementation from
https://gg thub. wtwttt/.coo/rryytt//aauuuueeeeee httppp/yaaaaatccuuaabbberttt-aae pytto and the implementation from https
//github.
wttbooiiiaal__eeeeee__ text
28

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D Dataset
D.I The International Stroke Trial database
The International Stroke Trial (IST) 53 was conducted between 1991 and 1996. It is one of the largest randomized controlled trials acute stroke treatment. The dataset comprises 19, 435 patients from 467 hospitals across 36 countries, enrolled within 48 hours of stroke onset. The treatment assignments include (trial aspirin allocation and trial heparin allocation with levels for medium dose, low dose, and no heparin). The outcome is whether patient will experience stroke again. Covariates include age, sex, presence of atrial fibrillation, systolic blood pressure, infarct visibility on CT, prior heparin use, prior aspirin use, and recorded deficits of different body parts.
D.2 MIMIC-III dataset
The Medical Information Mart for Intensive Care (MIMIC-III) (30) is a large, single-center database comprising information relating patients admitted critical care units at large tertiary care hospi- tal. MIMIC-III contains 38, 597 distinct adult patients. We follow the standardized preprocessing pipeline 63 of the MIMIC-III dataset. We use demographic variables such as gender and age, along with clinical variables including vital signs and laboratory test results upon admission, confounders. These include glucose, hematocrit, creatinine. sodium, blood urea nitrogen, hemoglobin, heart rate, mean blood pressure, platelets, respiratory rate, bicarbonate, red blood cell count, and anion gap, among others. The binary treatment is mechanical ventilation. The outcome the number of days patient needs to stay in the hospital.
We follow previous work
39
54
33
simulate treatment and outcome follows
{
A ~ (ernoulli(O.)).
(38)
Y = o + 7A + 72.42 + 73 (in( + (5" X) A A+G
where B, 8, Y1. 72, and 7/3 are fixed coefficients; a refers to the sigmoid function ((2) Ite and c noise term.
D.3 Experiments with varying confounder strengths
To evaluate the performance of TCA under varying confounder strengths, modify the data- generating process by introducing scaling parameters that control the influence of the confounders X on the outcome Y and/or the treatment assignment A.
Specifically, for the outcome model, we use a scaling parameter v) 2 O, which controls the strength of the confounding effect via the interaction term (6 X)A. Setting т O corresponds to no confounding from this term, whereas larger values of rj induce stronger confounding. Alternatively, to also vary the confounding in the treatment assignment, we can generate A via logistic model
P
[x,
(39)
where k 2 0 regulates the influence of X on A, E is parameter vector, and ((2) 1te- denotes the sigmoid function.
In experiments, vary 17 (and/or k) over predetermined grid (e.g.. 7) E {0.5, 1.0, 1.5)) to simulate different levels of confounder strength. As the confounding strength increases, we expect that the naïve text-based estimator (which relies solely on the generated text surrogate and assumes unconfoundedhess) will ineur increasing bias due unadjusted residual confounding. In contrast, TCA framework leverages the full confounder X for robust nuisance function estimation and employs a doubly-robust regression that conditions on Í. Therefore, our method should remain robust, which should thus widen the performance gap in favor of TCA as the confounder strength increases.
Prediction performance under varying confounder strength: When the true confounder strength is low, both our TCA framework and the naive text-based estimators are expected to yield comparable CATE estimates since residual confounding is minimal. However, as the confounding strength increases, the naïve estimators which rely solely on the generated text surrogate T and assume unconfoundedness will incur greater bias due to unadjusted residual confounding. In contrast, method leverages the
29

---

full confounder X for robust nuisance function estimation and employs doubly-robust regression that conditions on T. As a result, TCA exhibits significantly improved performance under strong confounding, leading an increasing performance gap between our method and the naïve baselines as confounder strength increases.
D.4 Experiments with varying prompt strategies
We test three prompt families to generate 7.
(i) Factual prompt:
Template example:
Transfer this patient information into a paragraph of text. We have the patient clinical information including gender, age, glucose, hematocrit, creatinine, sodium, blood urea nitrogen, hemoglobin, heart rate, mean blood pressure, platelets, respiratory rate, bicarbonate, red blood cell count, and anion gap. The values M,41, 129, ., 45.4,,,,.22, M,41,129.0,44.2,,1 140. 1...0,........................0. 15.4, 10.5.4,8400,, 103. 258 0,, 0,, 43,
Example output:
The patient is 41-year-old male with a glucose level of 129.0 0 mg/dL. His hematocrit is 45.2%, and creatinine measured at 1.2 mg/dL. The sodium level is 140.0 mEq/L, while the blood urea nitrogen is 16.0 mg/dL. He has a hemoglobin level of 15.4 g/dL, a heart rate 84.0 beats per minute, and a mean blood pressure of 103.4 4 mmHg The patient's platelet count is 258.0 109/L, with a respiratory rate of 12.0 breaths per minute. His bicarbonate level is 30.0 mEq/L, and his red blood cell count 5.46 million cells per μL, resulting in an anion gap of 13.0 mEq/L.
(ii) Narrative prompt:
Template example:
Write a detailed clinical narrative for patient with these features:
gender, age, glucose, hematocrit, creatinine, sodium, blood urea nitrogen, hemoglobin, heart rate, mean blood pressure, platelets, respiratory rate, bicarbonate, red blood cell count, and anion gap. The values F, 2,65,214.0 29, 11. 138.0,,2.0,99... 83.
15. 4,,,
30

---

Example output:
The patient, a 65-year-old female, presents with significant hyperglycemia, indicated by glucose level of 214.0 mg/dL, which suggesta poor blood sugar control. Other vital signs reflect some abnormalities; her hematocrit is elevated at 29.55%, indicating possible dehydration or underlying blood issues. The creatinine level at 11.7 mg/dL is concerning for potential kidney dysfunction, while sodium is within the normal range at 138.0 0 mmol/L. The blood urea nitrogen is raised at 62.0 mg/dL, which might further point renal impairment or dehydration. Her heart rate is slightly elevated at 83.0 beats per minute, and mean blood pressure on the lower side at 77.0 mmlig, which could suggest compromised cardiovascular status or shock. The platelet count high at 315.0 x10"/L, and respiratory rate and bicarbonate levels appear stable. Overall, the patient is likely feeling unwell, with symptoms potentially related to high blood sugar levels and possible kidney issues, and she may require further evaluation and management to address these concerning lab results. Access to healthcare facilities for comprehensive diagnostic testing and treatment essential this stage.
(iii) Symptom-focused prompt:
Template example:
Here is a patient current information (if this patient can use diagnostic equipment or has access healthcare facilities): The variables include gender, age, glucose, hematocrit, creatinine, sodium, blood urea nitrogen, hemoglobin, heart rate, mean blood pressure, platelets, respiratory rate, bicarbonate, red blood cell count, and anion gap. The values are M,76 198.199.55 26,566666 27.277.03. 03,266,,66200.. 2,,,2,6,122.... 67, 24,,442.8.77 14,,0,,001111 233. 233.6,16.0, 0,.,6.0016...... 33, 1,,142.67
2. 2.88,08 20,8,20.00 .7. How would this patient describe his/her symptoms via text with just partial diagnostic measurements?
Example output:
I've been feeling really unsell lately. I'm 76-year-old man, and I've noticed I've been more tired than usual, with a lack of energy that makes it difficult to engage in my daily activities. There's this constant feeling of thirst that can't shake off, and seem to be going to the bathroom racing at times, and get short of breath, especially when try to around do any little tasks. Sometimes, experience strange sensation in my head, almost like dizziness or confusion. I've also noticed frustrated not being able to enjoy my usual routine or activities. I just want to feel better and get back to my normal life.
often. My heart feels like it's
swelling in my legs. It worries me, and I feel
Expected performance differences between prompting technqiues: Since our method is designed to correct for the residual confounding by using the full confounders is inherently more robust variations in prompt quality. In contrast, the naive baseline directly relies on T for adjustment and therefore is more sensitive to the quality and informativeness of the text. With low-quality prompts (i.e., the factual prompt), both methods might perform similarly poorly, though our method is still expected to have an advantage due to its use of X. With high-quality prompts (i.e., the narrative or cymptom-focused) the performance our method is expected to improve substantially because it can fully exploit the richer text representations to accurately map onto the
31

---

treatment effects. Meanwhile, the naive baseline remains biased due to its inability to adjust for the missing confounder components from X.
32

---

E Additional acknowledge
We have access ChatGPT Edu through our university, which is SOC Type compliant (https
7/openai.. http////e/e/toduuunnneeeeeee chattptthhhtttt--aee edu/). This gives us enhanced privacy controls available in the standard API, free version, or even ChatGPT Team.
As part of this setup, we have applied for a Business Associate Agreement (BAA) (https:
to gain access to Zero Data Retention
(ZDD).. With ZDR enabled, the store parameter for fo1/ will always be treated as false https://platforms openai. com/ docs guides/your In this setup. OpenAl does not retain any request or response data after processing, meaning there is no storage, no logging, and, more importantly, no human review. This configuration provides privacy approach that functionally equivalent the one recommended by PhysioNet https://physions:: where Azure OpenAl is used with human review explicitly disabled. We confirm that our analysis thus HIPAA--cmplianl and thus in line with the PhysioNet rules.
and v1/ vt/chattcoompetionnt completions
org/newws news/ http:/wwwwsssse/eeeeeeeees gpt-resporsssss sesoonssllllue use
33

---

Further discussion
Our framework leverages LLM-derived text to capture an induced text confounder during training. Hence, we acknowledge risks from LLM use, such as biases or data misrepresentation. We thus advocate for rigorous validation of LLM outputs with domain experts ensure reliability and safety, particularly in high-stakes contexts in medicine. Further, LLMs should be used transparently and ethi- cally [15), with safeguards against amplifying societal biases. Future work should explore integrating uncertainty quantification to further increase trust in our TCA during real-world deployment.
Technically, our method relies the fact that LLMs generate faithful representations, meaning that generated text I' and embedding preserve semantic relationships. Importantly, such challenges are not unique to our method; any baseline relying on text generation or embeddings would similarly be impacted by low-quality text suboptimal embeddings. This highlights the broader importance of leveraging robust LLMs and embedding techniques in text-based causal inference tasks.
proposed framework has also benefits for making reliable inferences. By addressing inference time text confounding, our framework focuses on an important but overlooked setting where the true text confounders missing inference time. Such setting is common in telemedicine (e.g., when LLMs are used for making treatment recommendations where access to physicians is often limited where access to diagnostic infrastructure is lacking). Here, framework can help reduce bias in CATE estimation and can thus help improve personalized medicine.
Notwithstanding,
34


Reviews:
[{"review_id": "B4YAQ7PX2X", "reviewer": "Reviewer_FZSB", "summary": "The paper tackles the challenge (which they refer to as \"inference time text confounding\") when CATE estimation is done at inference time using text and confounders are not observed, but they are observed during training. The paper provides a framework for CATE estimation in this setting, and shows its real-world utility.", "questions": "(1) Can you please explain what are several real-world settings where inference-time text confounding applies, and specifically what are the interventions and outcomes we would care about that would motivate CATE estimation\n(2) How does your method compare to strong baselines (e.g., CaML, and SIN [2])\n\n[2] https://proceedings.neurips.cc/paper/2021/file/d02e9bdc27a894e882fa0c9055c99722-Supplemental.pdf", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n* The paper is very clearly written and the problem setting is clear and well-formulated \n* Strong performance results compared to baselines. The robustness across varying conditions (confounder strengths and prompt strategies) enhances the credibility of results.\n* The paper is highly reproducible\n\nWeaknesses\n* The TCA method seems to be a straightforward application of existing methodologies (meta-learners and DR pseudo-outcomes)\n* While \"Inference Time Text Confounding\" is common in medical domains, it's not fully clear to me how this connects to CATE estimation. For example in Figure 1 I don't understand what CATE is. CATE estimation is useful when we are trying to decide on an intervention. But what is the *intervention*  in this case?\n* Stronger baselines could be included. For example, the framework seems conceptually very similar to the CaML framework, in that you are (1) getting pseudo-outcomes as a pre-processing step, then (2) generating embeddings (3) Predicting the pseudo-outcomes from the embeddings. How does the performance of would this compare to using CaML out of the box?  \n\n[1]https://arxiv.org/pdf/2301.12292", "was_revised": true, "final_justification": "I thank the authors for their responses to my concerns, including w.r.t to novelty, baselines, as well as real world motivation. Authors have sufficiently addressed my concerns on baselines and novelty, and I appreciate the inclusion of new causal baselines to the paper. I have also thought through the author’s explanations on the real-world motivation of the inference time confounding problem. Upon reflection, I believe it is indeed a real-world problem since it is not always possible to quickly obtain covariates such as test results and diagnostics, and in emergency settings it is sometime needed to make decisions before obtaining covariates. However, I would strongly encourage authors in their final version to de-emphasize the telemedicine and chatbot motivation, since making personalized treatment decisions based on chatbots is extremely dangerous, as is telemedicine in which doctors are not soliciting information from patients but rather making decisions based on incomplete covariates. I have updated my score accordingly.", "rebuttal": "Thank you for your constructive review and your helpful comments! We are very happy to answer your questions and improve our paper as a result.\n\n\n### Response to weakness\n\n**Response to W1**\n\nThank you for the comment.  We would like to clarify that **TCA is not a straightforward application** of standard meta-learners or doubly robust (DR) learners.\n\nOur contributions go beyond existing methodologies in the following ways:\n\n(1) **Problem formalization:** We are the first to formally define the inference-time text confounding problem and show that naïve applications of existing meta-learners are biased. We derive **pointwise confounding bias** for these baselines, highlighting their limitations.\n\n\n(2) **Theoretical insight:** We provide the **identifiability result** connecting the target CATE under text ($\\tau^t(t)$) to the CATE under structured confounders($\\tau^x(x)$), and propose a two-stage estimation procedure to adjust for inference-time confounding. This theoretical connection is **non-trivial** and not addressed in prior work.\n\n\n(3) **Practical framework:** We introduce an **LLM-driven pipeline** to generate text and translate the theoretical result to a novel, practical framework for dealing with real-world conditions where structured data is unavailable at inference time.\n\n\nIn contrast, **the baselines we include in our experiments are direct and straightforward applications of existing meta-learners using text as input**. Even though they share the same initial text-generation step (using LLMs), they **remain biased**, as demonstrated both **theoretically** (Lemma 4.1) and **empirically in our experiments** (Table 1). ] _A simple off-the-shelf combination of the text-generation step with existing DR-learners would be biased_. \n\nTo show this empirically, **we performed new experiments against an off-the-shelf combination of our text-generation step and several more meta-learner baselines, including DR-learner**, X-learner, and R-learner. We call these baselines **“TBE-DR”**, “TBE-X”, “TBE-R”. Importantly, we made the experiment _fair_: we used the same setup for the LLMs, etc., so that the performance gains must be attributed to our framework. We again report PEHE scores on two datasets. \n\n| Dataset | TBE-DR | TBE-X | TBE-R | TCA (ours) |\n|---------|--------|-------|-------|------------|\n| IST     | 0.187  | 0.195 | 0.194 | 0.116      |\n| MIMIC   | 0.266  | 0.274 | 0.277 | 0.179      |\n\n=> **Our TCA shows consistent improvements by a large margin**. \n\n\nOur method outperforms the TBE-DR baselines despite using the **same underlying estimation technique**—highlighting that the core contribution lies in **how we adjust for confounding**, not in the DR method itself.  Our method also outperforms other straightforward applications of existing meta-learner baselines. This demonstrates the effectiveness of our TCA framework and proves our method **differs substantially** from existing methodologies (meta-learners and DR pseudo-outcomes).\n\n\n**Action**: We will add the new experiments to our paper. \n\n**Response to W2**\n\nThank you for the question.  In Figure 1, we illustrate a common setup from clinical datasets such as the IST dataset. \nThe intervention in this example is a binary treatment: whether the patient receives the treatment assignment of aspirin allocation or not. The outcome is whether the patient experiences a recurrent stroke. Patient covariates, which are only observed directly during training time, include demographic variables (e.g., gender, age) and clinical measurements (e.g., presence of atrial fibrillation, blood pressure,  weakness in arms/legs). In this context, the CATE is the expected difference in the probability of having a stroke recurrence if a patient with covariates $X=x$ were given aspirin versus or not.\n\n=> The motivation is that such CATE estimates help inform personalized treatment decisions: for which subgroups of patients is certain medicine beneficial, and for which it may not help much. \n\nThe challenge we focus on in the paper is that, at inference time, when a new patient comes to ask about his/her case via telemedicine or remote healthcare consultations, or medical chatbots), the structured features may not be available (e.g., due to the limitation of diagnostic tests or clinical measurements,  sensor limitations, or privacy constraints), but this patient can describe his/her feeling. CATE predictions are made using only textual descriptions with self-reported symptoms of the patient. \n\nOur framework is designed for this setup: to address this **inference-time text confounding** setting by learning to estimate CATE from structured data at training time, and using text at inference time, while adjusting for confounding via the induced confounding information in the text.\n\n\n**Response to W3**\n\nThank you for the suggestion. We would like to clarify that SIN [1] and CaML [2] focus on a **different setting** and that both are *not* meaningful baselines from a theoretical and empirical point. \n\nThe baselines (SIN and CaML) focus on a **different causal graph**: these methods focus with _high-dimensional_ **treatments** (e.g., predicting treatment effects of a new drug by using the molecular structure or a text description), while our settings focus on _high-dimensional_ **confounders** (e.g., the text descriptions is what we use to adjust for confounding). Hence, the way of how high-dimensional information enters the machine pipeline is also different. This fundamental difference leads to **different causal graphs** and objectives. \n\nThere are also some further differences: (i) Our method does not require additional information on complex treatments. (ii) We focus on mitigating inference-time confounding, whereas SIN and CaML are developed for estimating treatment effects of complex structured treatments (text or molecules). (iii) We additionally assume that text is only available at inference time, not at training time (while, for SIN and CaML, it is mandatory that text is available at training time).  Especially because of reason (iii), both baselines are **not directly applicable** to our setting.\n\nIn addition to the theoretical explanation, we would like to provide empirical evidence. Although these methods are not directly applicable to our setting, we adapted their architectures for comparison. This means we use their architectures but integrate them into our LLM pipeline (e.g., we combine their architecture with the text generation step from our TCA framework). Further, for SIN[1], we use their method design to construct pseudo-outcomes and then regress on the text generated by LLMs to simulate missing treatment information. For CaML[2], as it is a zero-shot approach that requires additional structured information for a new treatment as input, we cannot apply it in its original form. Instead, we take its model architecture to predict CATE. The results for both IST and MIMIC-III datasets are below (reported: PEHE). **The results show that our method outperforms the baselines**.\n\n\n| Dataset         | CaML  | SIN   | TCA (ours) |\n|----------------|-------|-------|------------|\n| IST   | 0.201 | 0.195 | **0.116**      |\n| MIMIC  | 0.285 | 0.278 | **0.179**      |\n\n\n\n**Action**: We will add the above comparison to our revised paper.\n\nReference:\n\n[1] Kaddour, J., Zhu, Y., Liu, Q., Kusner, M.J. and Silva, R. Causal effect inference for structured treatments. Advances in Neural Information Processing Systems, 2021.\n\n[2] Nilforoshan, H., Moor, M., Roohani, Y., Chen, Y., Šurina, A., Yasunaga, M., Oblak, S. and Leskovec, J.. Zero-shot causal learning. Advances in Neural Information Processing Systems, 2023."}, {"review_id": "wcJkqh1uFQ", "reviewer": "Reviewer_JPDz", "summary": "Estimating treatment effects is an important task in many empirical sciences such as healthcare and political science. However, the information available when we seek to estimate treatment effects may not always be sufficient. Specifically, at inference time, we may only have access to text data that are noisy proxies of the actual confounders between treatment and outcome. The authors propose a novel methodology for estimating treatment effects when practitioners have access to only covariates at training time and only text data at inference time. The authors demonstrate the effectiveness of their method through semi-synthetic experiments.", "questions": "In Appendix D describing implementation details for the semi-synthetic experiments, the authors state that they used OpenAI’s APIs to generate surrogate text data. This implies that the authors passed covariate data from MIMIC-III through OpenAI’s API, which violates MIMIC-III’s terms and conditions (https://physionet.org/news/post/gpt-responsible-use). Can the authors verify whether they passed protected information through OpenAI’s API? I will raise my score if the authors are able to address this potential ethics violation.\n\nWhat happens if the function $h$ for generating text data from covariates differs between training time and inference time? For instance, suppose the LLMs generate text data significantly differently from the way health practitioners write their notes. Would the authors expect downstream estimates for the treatment effect to be biased in this case? \n\nWhat happens if, at inference time, text data is affected by the treatment and/or outcome? For instance, suppose a practitioner used discharge notes as $T$ at inference time, then the text data may likely be a child of both $A$ and $Y$, and conditioning on $T$ will induce confounding between the treatment and outcome. Is this something that practitioners need to worry about when considering the authors’ method? What are steps that practitioners can take to mitigate this concern?\n\nIn datasets such as MIMIC-III, clinical text data in addition to covariate data may be available at training time. In such a scenario, would the authors’ proposed method still apply by using the available text data directly to learn $f_\\theta$? Would it still be necessary to generate surrogate text data $\\tilde{T}$ in this case? Would the variability of downstream estimates for the conditional average treatment effect be improved in this case?\n\nDoes line 198 have a typo? Instead of “Y(1) if the treated”, did the authors mean to write “Y(1) if treated”?\n\nDoes line 214 have a typo? The final sentence seems to be incomplete and does not have any ending punctuation.", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\nOne clear strength of this paper is its clarity. The authors explain clearly how their problem setting differs from problem settings considered previously in the literature. They also give a clear example (Figure 1) of when covariate data may be available at training time but not at inference time. Throughout the paper, the authors also do a great job introducing their problem setup, why naive approaches fail, and the assumptions required for their novel TCA framework to be valid.\n\nThe observation that generating synthetic text data at training time to learn a function on how average treatment effects vary based on text data to aid in the estimation of treatment effects at inference time is an interesting observation. As large language models improve in quality, it is great to see methods such as the authors’ that use them without relying on large language models’ reasoning abilities, which has been shown to be limited and flawed.\n\nPrevious work that considers text data as a proxy for estimating treatment effects requires two proxies and two separate and independent pieces of text data. In the authors’ method, they only require one instance of text data. Even though their method requires a training dataset with access to the ground truth covariates, a condition not required in previous work, the authors’ method applies to a new, and realistic, scenario and is thus valuable to the community. Practitioners now have more methods to choose from depending on the specific dataset that they have.\n\nThe semi-synthetic experiments are promising and demonstrate how different prompts to large language models affect the downstream accuracy of the authors’ proposed method pipeline. Table 2 also demonstrates that potential biases that may be present in LLMs do not significantly impact the fairness of downstream estimates for the treatment effect.\n\nWeaknesses\n\nI have no major complaints regarding this paper, but I have some questions below that I hope the authors can answer and discuss.", "was_revised": true, "final_justification": "The authors take a principled approach to address a realistic problem that occurs when covariate data is available and text data is not available at training time but covariate data is not available and text data is available at testing time. The paper is well-written, and, through data studies, the authors show the effectiveness of their method. Although there was an initial ethics concern regarding the use of protected healthcare data in OpenAI's API, the authors have addressed this concern and verified that their data anlysis conforms with the terms and conditions of their dataset. Thus, I recommend acceptance of this paper.", "rebuttal": "Thank you for your positive review and your helpful comments! We are very happy to answer your questions and improve our paper as a result.\n\n### Response to Questions\n\n**Answer to Q1**\n\nThank you for your important question.\n\nWe have access to ChatGPT Edu through our university, which is SOC 2 Type II compliant [1]. This gives us enhanced privacy controls not available in the standard API, free version, or even ChatGPT Team.\n\nAs part of this setup, we have applied for a Business Associate Agreement (BAA) [2] to gain access to Zero Data Retention (ZDR). With ZDR enabled, the store parameter for /v1/responses and v1/chat/completions will always be treated as false [3]. In this setup, OpenAI does not retain any request or response data after processing—meaning there is no storage, no logging, and, more importantly, no human review.\n\nThis configuration provides a privacy approach that is functionally equivalent to the one recommended by PhysioNet [4], where Azure OpenAI is used with human review explicitly disabled.\n\n=> We confirm that our analysis is thus HIPAA-compliant and thus in line with the PhysioNet rules.\n\n[1] https://openai.com/index/introducing-chatgpt-edu/\n\n[2]https://help.openai.com/en/articles/8660679-how-can-i-get-a-business-associate-agreement-baa-with-openai-for-the-api-services\n\n[3] https://platform.openai.com/docs/guides/your-data#zero-data-retention\n\n[4] https://physionet.org/news/post/gpt-responsible-use\n\n\n\n**Answer to Q2**\n\nThank you for raising this important point.\n\n We acknowledge that LLM-generated text may not perfectly align with real-world text distributions and that a domain shift between training-time and inference-time text can occur in practice. Such shifts can affect methods’ performance.\n\nTo evaluate this scenario, we conducted robustness experiments by varying the LLM prompt strategies used to generate text across datasets (Figure 3(b)). This introduces a controlled distribution shift in the text inputs. Despite these differences, our method consistently outperforms all baselines, demonstrating its robustness to moderate levels of text distribution mismatch.\n\nWhile discrepancies between the surrogate text $\\tilde{T}$ and the true inference-time distribution $T$ may exist, they can potentially be mitigated through domain adaptation techniques. For instance, if a small sample of real-world text $T$ is available, one could adapt the generation process of $\\tilde{T}$ to better align with it. We consider this a promising direction for future work.\n\nFor example, there are several potential e strategies for extending our approach when a few text samples from the test distribution are available:  (i) In-context alignment: One strategy is to incorporate these additional samples directly into the prompt, guiding the LLM with instructions such as: \"Here are example descriptions. Please formulate your descriptions in a similar style, level of detail, etc.\" (ii) Alignment via fine-tuning: Anothe strategy is fine-tuneing the LLM by introducing a loss term that minimizes the distributional distance between generated text and the test distribution samples—for example, using Wasserstein distance or Maximum Mean Discrepancy (MMD) as the alignment loss.\n\n\n**Answer to Q3**\n\nThank you for highlighting this important point. We fully agree that if text data is generated after treatment assignment or outcome realization, it may act as a **post-treatment** variable (e.g., mediators), and adjusting for such variables can introduce collider bias or post-treatment bias.\n\nIn our work, the $T$ used is only the set of **pre-treatment** variables that serve as confounders. In practice, it is crucial for practitioners to apply domain knowledge to identify and exclude post-treatment text from the adjustment set for inference data. This is typically feasible in real-world clinical settings, as most health records are timestamped, making it possible to determine whether a variable was recorded before or after treatment assignment. \n\n**Answer to Q4**\n\nThank you for the insightful question. Yes, if clinical text data is available at training time, our method can still be applied.\n\n\nIf the clinical text contains additional confounding information that is not captured in the structured covariates, we can include this text together with the covariates $X$ as an additional input to the text generation (i.e., the prompt could look like “A patient with features $X$ and the following reported self-reported symptoms[...]”) in the estimation procedure to avoid confounding bias. Further, this additional information helps to improve the accuracy of CATE estimation in downstream estimates. This flexibility resembles another strength of our method.\n\n\n However, if the clinical text contains only confounding information that is also captured in $X$ or is less informative, but follows a different distribution compared to the inference-time text (e.g., different style or domain), then this text contains only noisy information that does not improve estimation. Thus, such text should not be considered, and we can apply our framework as is, using only the covariates $X$, which thus still requires generating surrogate text data. \n\n**Answer to Q5, 6**\nThank you! We will fix the typos."}, {"review_id": "ZcNqTalPdS", "reviewer": "Reviewer_kWL2", "summary": "This paper proposes a method for causal effect estimation when confounder values are observed as textual description at test/inference time. The proposed problem setup and the solution proposed are novel and are first of its kind.The proposed method has real-world use cases especially in healthcare domains. The proposed idea identifies conditional average treatment effect conditional on textual confounders using the conditional average treatment effect conditional on actual/numerical values of confounders. Experimental results presented show improvements over naive baselines.", "questions": "Please see the weaknesses section and provide your responses.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The proposed problem setup of causal effect estimation under test time text confounders is novel, and has real-world significance.\n2. The proposed solution is intuitive and theoretically grounded.\n3. The experimental results show good performance compared to baselines in all experimental setups.\n\nWeaknesses:\n\n1. In the abstract the following line overstates the contributions as it is well known fact.\n\n\"We show that the discrepancy between the data available during training time and inference time can lead to biased estimates of treatment effects.\"\n\n2. A major issue with the current problem setup is that all covariates X are considered to be confounders. This holds very rarely in real-world. Many variables should not be used for adjustment as there may be covariates that are descendants of the treatment variable.\n\n3. The significance of Lemma 4.1 is unknown because it is obvious that the bias of naive estimator is positive.\n\n4. Finally, the proposed method tries to estimates $\\tau^t(t)$ instead of $\\tau^x(x)$. In my opinion, identifying $\\tau^x(x)$ at test time would be even more interesting. At least comparing the estimated values of  $\\tau^t(t)$ and $\\tau^x(x)$ in \"in-sample\" data and checking that they do not deviate much from each other would be a great addition to the paper and validates the idea of estimating $\\tau^t(t)$.", "was_revised": true, "final_justification": "All my concerns are addressed and I hope the assumption, which is strong, that all covariates are assumed to be confounders, is made more explicit in the revision. I will still keep my positive score despite the strong assumption because of the immediate real-world applicability of the work.", "rebuttal": "Thank you for your positive review and your helpful comments! We are very happy to answer your questions and improve our paper based on your suggestions. Below, we address your comments in detail. \n\n### Response to Weakness\n\n**Response to W1**\n\nThank you for the suggestion. We agree that the original phrasing in the abstract may have overstated what is already known. Our intention was to highlight that we formally define this issue under inference-time text confounding, which, to the best of our knowledge, has not been previously studied in the context of CATE estimation. We will revise the abstract to better reflect our contribution.\n\n**Response to W2**\n\nThank you for the insightful question. You are right that if the text used at inference time is affected by the treatment and/or outcome (e.g., discharge notes written after given treatment decisions), it may act as a post-treatment variable. Conditioning on such variables can induce post-treatment bias, as they may lie on causal paths from treatment to outcome (e.g., mediators or colliders).\n\nHence, we agree that, in real-world datasets, not all observed covariates should be treated as confounders—some may indeed be **post-treatment** variables (e.g., mediators) and should not be used for adjustment. We will thus add a discussion with practical considerations to help practitioners identify post-treatment variables, which should not be used for adjustment. \n\nIn our paper, we explicitly define $X$ as the set of **pre-treatment** variables that serve as confounders, in line with standard assumptions in the CATE estimation literature. Our method assumes that only such pre-treatment variables—whether structured or textual—are used for adjustment. \n\nIn practice, it is crucial for practitioners to apply domain knowledge to identify and exclude post-treatment variables from the adjustment set. This is typically feasible in real-world clinical settings, as most records (including textual notes) are timestamped, making it possible to determine whether a variable was recorded before or after treatment assignment. \n\n**Response to W3**\n\nThank you for the comment. While it may be intuitively expected that the naïve estimator is biased under inference-time text confounding, the goal of Lemma 4.1 is to formally formalize the pointwise bias. \n\nLemma 4.1 provides an explicit expression that quantifies how the omission of confounding information (when using $T$ instead of $X$) affects the estimated treatment effect. This formal result is important for rigorously establishing the limitations of naïve estimators that use text representations without proper adjustment.\n\nWe provide this intuition in Remark 4.2, which highlights that, in many real-world settings, the text variable $T$ often captures only a subset of the information in $X$, resulting in $Y(a) \\not\\perp A \\mid T$. As a result, the naïve estimator is biased, and this bias is typically positive under common conditions. We will revise the paper to better emphasize that Lemma 4.1 is not just stating that bias exists, but rather providing a precise mathematical form of that bias.\n\n**Response to W4**\n\nThank you for the insightful comment. We agree that identifying $\\tau^x(x)$ at test time would be even more interesting.  However, in our setting, confounder $X$ is not accessible at inference time, which makes identification of $\\tau^x(x)$ fundamentally impossible. \n\nWe appreciate your suggestion and have incorporated it. We **conducted the comparison between estimated $\\hat{\\tau}^t(t)$ against the ground-truth $\\tau^x(x)$** in in-sample data. We report both the averaged bias $\\mathbb{E}\\left[\\hat{\\tau}^t(t) - \\tau^x(x)\\right]$ and the root mean squared error (RMSE) $\\sqrt{\\mathbb{E}\\left[\\left(\\hat{\\tau}^t(t) - \\tau^x(x)\\right)^2\\right]}$ as metrics.  The results for both the IST and MIMIC-III datasets across several baselines are below.\n\n| Method         | Averaged Bias | RMSE  |\n|----------------|----------------|--------|\n| TBE-Slearner   | 0.0628         | 0.198 |\n| TBE-Tlearner   | 0.0621         | 0.197 |\n| TBE-DRlearner  | 0.0495         | 0.186 |\n| Ours (TCA)     | **0.0140**     | **0.114** |\n\n| Method         | Averaged Bias | RMSE  |\n|----------------|----------------|--------|\n| TBE-Slearner   | 0.0763         | 0.283 |\n| TBE-Tlearner   | 0.0759         | 0.282 |\n| TBE-DRlearner  | 0.0671         | 0.265 |\n| Ours (TCA)     | **0.0204**     | **0.177** |\n\n\nThis comparison provides empirical evidence that our method produces consistent and accurate approximations of $\\tau^x(x)$, with minimal deviation, thereby validating the effectiveness of our approach.\n\n**Action**: We will add the above experiments to our revised paper."}, {"review_id": "d8iESHcNxx", "reviewer": "Reviewer_zRnF", "summary": "This paper investigate the CATE estimation problem with text information during inference time while the complete confounder is available during training time. To bridge this gap, the authors first train a doubly robust estimator using the training dataset. Then, the author construct text description of the confounders in the training data. Based on the generated text, the algorithm constructs a mapping from the text to the estimated treatment effect. Experiemental results on International Stroke Trial and MIMIC-III demonstrate the effectiveness of the method.", "questions": "1. The Assumption 3.2 is disjointed with the other parts of the paper. How to understand the noise variable in the text generation mechanism?\n\n2. How to obtain the text of the test set in MIMIC-III and IST?\n\n3. The proposed TCA method use doubly robust method as the estimation. However, the baselines does not use doubly robust method. For fair comparision, I suggest to supplment the TBE-Doubly Robust as the baseline.", "limitations": "The authors present the limitations in Appendix G. As the authors claimed, the quality of the generated text and representation is important to the effectiveness of the method. And the other point is also important. Since the generated text contains less information than the confounder variable $X$, the single value of $T$ refers to multiple value of $X$. However, this fact is not considered in the method and a deterministic mapping is learned. Therefore, intergrating uncertainty quantification is necessary to this method.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The paper is well written and presented. The experimental results demonstrate the significant advancement of the proposed method. However, I think some weaknesses still exist as follows:\n\n1. The motivation is somehow weak. Because the text is much easier to obtain than complete measurement of confounders, it is not realistic that during training time, the text is unavailable.\n\n2. The novelty is not sufficient. The estimation method is the off-the-shelf doubly robust method. The incremental part is only the text generation and mapping function.\n\n3. The text generation based on LLM may be misaligned with the real text collection. Therefore, the rationality of the mapping function from the text to treatment effect is not convincing.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your constructive review and your helpful comments! \n\n\n### Response to Weakness\n\n**W1**\n\nWe appreciate the reviewer’s concern, but respectfully clarify that our setting—where text is unavailable during training but present at inference—is both realistic and increasingly common, especially in the medical domain.\n\nIn many real-world healthcare applications, it is common that structured data (e.g., demographics, vitals, labs) is reliably recorded and readily available during training, particularly in randomized controlled trials or publicly released clinical datasets. In contrast, unstructured text is often sparse or inaccessible, or incomplete, inconsistently formatted, and lacks standardization. \nStructured tabular data are carefully recorded and quality checked in clinical datasets, making them easily available. This is typically driven by regulatory need and approval for medical devices, where only high-quality information is preferred over self-reported texts. As a result, unstructured text is either unavailable, inconsistently documented. Models are often built on well-structured datasets from electronic health records or clinical trials. \n\nEven when some text is present during training (e.g., clinical notes), it is often incomplete, inconsistently formatted, and often differs substantially in style and content from inference-time text (e.g., from a different distribution and with language style). For example, training-time notes are authored by clinicians, while inference-time inputs in telemedicine, chatbots, or symptom-reporting apps are often patient-generated. Such free-text symptom descriptions often fail to cover the full set of confounding variables.\n\nOur setting, where models are trained on structured data but must make inferences on text data is both practically motivated and clinically relevant. Our work is the first to formally characterize the challenge of inference-time text confounding and to propose a concrete solution that is theoretically principled and empirically validated in such real-world conditions.\n\n**W2**\n\nWe would like to clarify that our method is **not** the off-the-shelf doubly robust method. Our TCA **differs substantially** from the standard DR-Learner. _A simple off-the-shelf combination of our text-generation step with existing DR-learners would be biased_.  \n\nOur main contribution includes\n\n(1) Problem formalization: We are the first to **formally define the inference-time text confounding problem**, which is distinct from standard causal inference settings. We show that naïve applications of existing methods (including DR learners) are biased in this setup, and we **derive their pointwise confounding bias**.\n\n(2) Theoretical contribution: We propose an estimation framework that connects the CATE $\\tau^x(x)$ w.r.t true confounder $X$ to the text-based CATE $\\tau^t(t)$, and we prove identifiability and unbiasedness of our estimator under this framework. This is not a trivial extension of existing DR methods.\n\n(3) Modular estimation strategy: While our approach leverages the doubly robust estimation to benefit from the doubly robust properties, it could also be easily adapted to use other meta-learners in the final step. \n\n(4) Empirical validation: We further **conduct new experiments** where we combine our text-generation pipeline with a standard DR learner (referred to as **TBE-DR**). Importantly, we made the experiment _fair_: we used the same setup for the LLMs, etc. We report PEHE scores on two datasets. \n\n| Dataset | TBE-DR | TCA (ours) |\n|---------|--------|------------|\n| IST     | 0.187  | 0.116      |\n| MIMIC   | 0.266  | 0.179      |\n\n=> **Our TCA outperforms TBE-DR by a large margin**. \n\nThis baseline uses the **same underlying estimation technique** as ours but lacks our framework for confounding adjustment. These results highlight that **the core innovation lies in our confounding adjustment framework**, not in the choice of DR itself. \n\n**W3**\n\nThank you for raising this important point. We acknowledge that LLM-generated text may not perfectly align with real-world text distributions. Our method is explicitly designed to tolerate such mismatches.\nTo assess robustness, we conducted domain shift experiments by varying the LLM prompt strategies across datasets (Figure 3(b)). Despite this shift, our method **consistently outperforms** all baselines, demonstrating **robustness** to the **distribution shift between text data**.\n\n### Response to Questions\n\n**Q1**\n\nThank you for your question. Assumption 3.2 defines the data-generating mechanism of the induced text confounder $T$, which is generated from the true confounder $X$ as $T = h(X, \\epsilon)$, where the noise variable satisfies $\\epsilon \\perp Y(a) \\mid X$. \nThis assumption formalizes the intuition that $T$ is a noisy (i.e., stochastic) transformation of the underlying true confounder $X$,  which reflects the nature of real-world text generation in clinical settings. For example:\n\n(1) Doctor’s notes or patient self-descriptions are often incomplete, informal, and subject to variability (noise) such as different writing styles, which are, however, independent of the outcome of interest.\n\n(2) LLM-generated text, even when conditioned on structured inputs, introduces randomness (e.g., due to sampling variability or temperature settings, or seed, etc.).\n\nThe noise variable $\\epsilon$ models this stochasticity in text generation, and allows diverse formats of the text description of the true confounder. \n\nImportantly, We do not require that $T$ preserves all relevant information from $X$. It also fits in applications where $T$ often does not capture all the necessary confounding information in $X$ for potential outcomes. This aligns with the imperfect, noisy, and incomplete nature of text data in practice. Instead, $T$ may contain only partial information about $X$. As $T$ is induced by $X$, the useful information encoded by $T$ should be included within the information contained in $X$ for predicting the outcome.\n\nBesides, the independence condition $\\epsilon \\perp Y(a) \\mid X$ ensures that:\nOnce we condition on $X$, the residual information in $T$ does not provide any additional information about the potential outcomes.  \n\nThis assumption is more for technical reasons to ensure identifiability, that is, identifiability of $\\tau^t(t)$ by adjusting for inference time text confounding, which allows us to express the CATE under text ($\\tau^t(t)$) as a conditional expectation of the true CATE ($\\tau^x(x)$), i.e., $\\tau^t(t)=\\mathbb{E}\\left[\\tau^x(X) \\mid T=t\\right]$. \nas shown in Lemma 4.3 and Appendix B.2. The assumption thus allows us to use our two-stage procedure for adjusting inference-time confounding when only $T$ is observed at test time.\n\n**Q2**\n\nThe text for our experiments on top of  MIMIC-III and IST is generated by LLMs to simulate text with self-reported symptoms. In the datasets, there is no text available that captures such symptom descriptions. Hence, we use LLMs to generate textual confounders $\\tilde{T}$ from structured clinical covariates $X$.  For each patient, we construct prompts by templating the key features of $X$ into natural language constraints. The generated texts are about 150–200 tokens long. We split the data into 80% train and 20% test.\n\n**Q3** \n\nThank you for the suggestion. We **added the TBE-Doubly Robust (TBE-DR) learner as another baseline**. The corresponding results are reported above under W2.\n\n### Response to Limitations\n\nThank you for the thoughtful comment. \n\nAssumption 3.2 formalizes the intuition that $T$ is a noisy (i.e., stochastic) transformation outcome of the underlying true confounder $X$.   We do not assume the mapping from $X$ to $T$ is deterministic, one-to-one, or invertible. Nor do we require $X$ to be reconstructed from $T$ or that $T$ preserves all confounding information. A single $T$ may correspond to multiple possible values of $X$.\n\nIt is true that the generated text $T$ often contains less information than the true confounder $X$, and this is **precisely why our framework is needed**—to adjust for inference-time confounding when only $T$ is available to use at inference time.\n\nTo study this systematically, we conduct experiments under varying levels of information preserved in $T$, which we refer to as high-info, medium-info, and low-info settings, based on how much information from $X$ is retained in the text. \n\nWe also appreciate the reviewer’s suggestion on incorporating uncertainty quantification. \nWe thus measure the discrepancy between estimated $\\hat{\\tau}^t(t)$ and ground-truth $\\tau^x(x)$. This discrepancy captures the uncertainty introduced by the loss of information in the transition from $X$ to $T$, introducing variability in $\\tau^t(t)$ relative to the true effect $\\tau^x(x)$. For reasons of space, we focus on TBE-DRlearner as it is the strongest baseline. We report results on IST and MIMIC, respectively. These results demonstrate that our framework is more robust to uncertainty.\n\n|Info Level|Method|$\\mathbb{E}[\\hat{\\tau}^t(t)-\\tau^x(x)]$|$\\sqrt{\\mathbb{E}\\left[\\left(\\hat{\\tau}^t(t) - \\tau^x(x)\\right)^2\\right]}$|\n|----------|------|-------------------------------|--------------------------------------|\n|High-info|TBE-DR|0.0320|0.134|\n|         |Ours (TCA)|**0.0106**|**0.085**|\n|Medium-info|TBE-DR|0.0302|0.160|\n|           |Ours (TCA)|**0.0124**|**0.097**|\n|Low-info|TBE-DR|0.0431|0.187|\n|        |Ours (TCA)|**0.0142**|**0.116**|\n\n\n|Info Level|Method|$\\mathbb{E}[\\hat{\\tau}^t(t)-\\tau^x(x)]$|$\\sqrt{\\mathbb{E}\\left[\\left(\\hat{\\tau}^t(t) - \\tau^x(x)\\right)^2\\right]}$|\n|----------|------|-------------------------------|--------------------------------------|\n|High-info|TBE-DR|0.0450|0.210|\n|         |Ours (TCA)|**0.0148**|**0.126**|\n|Medium-info|TBE-DR|0.0560|0.240|\n|           |Ours (TCA)|**0.0183**|**0.145**|\n|Low-info|TBE-DR|0.0673|0.266|\n|        |Ours (TCA)|**0.0206**|**0.179**|"}]

Author rebuttal:

Meta-review: All reviewers agree that this paper is clear, well-motivated and relevant in the context of the estimation of treatment effects. Two of the reviewers assigned a clearly positive score, particularly highlighting the novelty of the approach, the technical strength and the  real-world significance of causal effect estimation under test time text confounders.
The other two reviews are in the "borderline" area. These two reviewers mentioned some weaknesses, such as (some) weakly supported assumptions concerning data availability during training time and the (potentially) unrealistic assumption that all covariates are considered to be confounders. In the rebuttal, however the authors could successfully address most of these concerns in a plausible way. Therefore, I think that for this paper the positive aspects outweigh the weaknesses, and recommend acceptance of this paper
Decision: Accept (poster)