mlsanityData to 14 Oct 2026

Latent Variable Modeling for Robust Causal Effect Estimation

arXiv 2508.20259 ↗First posted 27 Aug 2025cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Latent variable models provide a powerful framework for incorporating and inferring unobserved factors in observational data. In causal inference, they help account for hidden factors influencing treatment or outcome, thereby addressing challenges posed by missing or unmeasured covariates. This paper proposes a new framework that integrates latent variable modeling into the double machine learning (DML) paradigm to enable robust causal effect estimation in the presence of such hidden factors. We consider two scenarios: one where a latent variable affects only the outcome, and another where it may influence both treatment and outcome. To ensure tractability, we incorporate latent variables only in the second stage of DML, separating representation learning from latent inference. We demonstrate the robustness and effectiveness of our method through extensive experiments on both synthetic and real-world datasets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 62 references that are in the map. First posted on arXiv 27 Aug 2025.

Builds on

19 of 62 references.

References · 19

  1. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 410
  2. 2020A Survey on Causal Inferencecited by 202
  3. 2019Causality matters in medical imagingcited by 193
  4. 2019Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processescited by 119
  5. 2020A unified survey of treatment effect heterogeneity modeling and uplift modelingcited by 36
  6. 2020Adversarial Estimation of Riesz Representerscited by 35
  7. 2022Estimating average causal effects from patient trajectoriescited by 29
  8. 2020Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimationcited by 27
  9. 2017Robust Synthetic Controlcited by 25
  10. 2021Long Story Short: Omitted Variable Bias in Causal Machine Learningcited by 23
  11. 2013Empirical entropy, minimax regret and minimax riskcited by 21
  12. 2020Causal Feature Selection via Orthogonal Searchcited by 12
7 more
  1. 2020Debiased Off-Policy Evaluation for Recommendation Systemscited by 9
  2. 2023A Cross-Moment Approach for Causal Effect Estimationcited by 9
  3. 2022Sample-Specific Root Causal Inference with Latent Variablescited by 6
  4. 2021Distributed Double Machine Learning with a Serverless Architecturecited by 5
  5. 2019Online Causal Structure Learning in the Presence of Latent Variablescited by 4
  6. 2024Doubly Robust Inference in Causal Latent Factor Modelscited by 4
  7. 2021Outlier-Resistant Estimators for Average Treatment Effect in Causal Inferencecited by 2

Built on by

Not cited yet.