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Conditional Average Treatment Effect Estimation Under Hidden Confounders

arXiv 2506.12304 ↗First posted 14 Jun 2025cs.LG stat.MLcited by 0

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Abstract (arXiv)

One of the major challenges in estimating conditional potential outcomes and conditional average treatment effects (CATE) is the presence of hidden confounders. Since testing for hidden confounders cannot be accomplished only with observational data, conditional unconfoundedness is commonly assumed in the literature of CATE estimation. Nevertheless, under this assumption, CATE estimation can be significantly biased due to the effects of unobserved confounders. In this work, we consider the case where in addition to a potentially large observational dataset, a small dataset from a randomized controlled trial (RCT) is available. Notably, we make no assumptions on the existence of any covariate information for the RCT dataset, we only require the outcomes to be observed. We propose a CATE estimation method based on a pseudo-confounder generator and a CATE model that aligns the learned potential outcomes from the observational data with those observed from the RCT. Our method is applicable to many practical scenarios of interest, particularly those where privacy is a concern (e.g., medical applications). Extensive numerical experiments are provided demonstrating the effectiveness of our approach for both synthetic and real-world datasets.

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References · 17

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  2. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
  3. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 158
  4. 2024Robust agents learn causal world modelscited by 81
  5. 2018Confounding-Robust Policy Improvementcited by 76
  6. 2024Causal machine learning for predicting treatment outcomescited by 74
  7. 2018Removing Hidden Confounding by Experimental Groundingcited by 64
  8. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  9. 2020Causal inference methods for combining randomized trials and observational studies: a reviewcited by 51
  10. 2022Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effectscited by 32
  11. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  12. 2020Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confoundingcited by 25
5 more
  1. 2021Sequential Deconfounding for Causal Inference with Unobserved Confounderscited by 24
  2. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  3. 2021Generalizing Off-Policy Learning under Sample Selection Biascited by 16
  4. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
  5. 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Datacited by 3

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