# Latent Variable Modeling for Robust Causal Effect Estimation

arXiv: https://arxiv.org/abs/2508.20259 · HTML: https://mlsanity.com/paper/2508.20259/

## 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 (most cited first, 19 of 19)

- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes](https://mlsanity.com/paper/1908.08526/) (2019)
- [A unified survey of treatment effect heterogeneity modeling and uplift modeling](https://mlsanity.com/paper/2007.12769/) (2020)
- [Adversarial Estimation of Riesz Representers](https://mlsanity.com/paper/2101.00009/) (2020)
- [Estimating average causal effects from patient trajectories](https://mlsanity.com/paper/2203.01228/) (2022)
- [Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation](https://mlsanity.com/paper/2002.07285/) (2020)
- [Robust Synthetic Control](https://mlsanity.com/paper/1711.06940/) (2017)
- [Long Story Short: Omitted Variable Bias in Causal Machine Learning](https://mlsanity.com/paper/2112.13398/) (2021)
- [Empirical entropy, minimax regret and minimax risk](https://mlsanity.com/paper/1308.1147/) (2013)
- [Causal Feature Selection via Orthogonal Search](https://mlsanity.com/paper/2007.02938/) (2020)
- [Debiased Off-Policy Evaluation for Recommendation Systems](https://mlsanity.com/paper/2002.08536/) (2020)
- [A Cross-Moment Approach for Causal Effect Estimation](https://mlsanity.com/paper/2306.06263/) (2023)
- [Sample-Specific Root Causal Inference with Latent Variables](https://mlsanity.com/paper/2210.15340/) (2022)
- [Distributed Double Machine Learning with a Serverless Architecture](https://mlsanity.com/paper/2101.04025/) (2021)
- [Online Causal Structure Learning in the Presence of Latent Variables](https://mlsanity.com/paper/1904.13247/) (2019)
- [Doubly Robust Inference in Causal Latent Factor Models](https://mlsanity.com/paper/2402.11652/) (2024)
- [Outlier-Resistant Estimators for Average Treatment Effect in Causal Inference](https://mlsanity.com/paper/2106.13946/) (2021)

## 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.