# Latent Variable Modeling for Robust Causal Effect Estimation

## TL;DR

This paper introduces a novel framework combining latent variable models with double machine learning to improve causal effect estimation when unobserved confounders are present, demonstrating robustness through extensive experiments.

## Contribution

It integrates latent variable modeling into DML, addressing unmeasured confounders in causal inference with a focus on separating representation learning from latent inference.

## Key findings

- Enhanced robustness in causal effect estimation with unobserved confounders.
- Effective separation of latent inference from representation learning.
- Validated performance on synthetic and real-world datasets.

## Abstract

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.