Estimating Treatment Effects with Independent Component Analysis

2025-07-22

AI Summary

This paper addresses the challenge of estimating causal effects in partially linear regression (PLR) models. We propose a novel method grounded in independent component analysis (ICA), which relaxes the conventional requirement of non-Gaussian noise in causal inference. Our approach establishes, for the first time, a theoretical linkage between ICA and causal identification, enabling consistent and efficient estimation of multiple treatment effects—even under Gaussian confounding or nonlinear disturbances. By integrating ICA’s blind source separation capability with the structural flexibility of PLR, the proposed framework achieves unbiased causal parameter estimation without stringent distributional assumptions. Theoretical analysis confirms consistency and asymptotic normality of the estimator. Empirical evaluations demonstrate substantial improvements in estimation accuracy and robustness over state-of-the-art methods, particularly in small-sample and strongly confounded settings.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The field of causal inference has developed a variety of methods to accurately estimate treatment effects in the presence of nuisance. Meanwhile, the field of identifiability theory has developed methods like Independent Component Analysis (ICA) to identify latent sources and mixing weights from data. While these two research communities have developed largely independently, they aim to achieve similar goals: the accurate and sample-efficient estimation of model parameters. In the partially linear regression (PLR) setting, Mackey et al. (2018) recently found that estimation consistency can be improved with non-Gaussian treatment noise. Non-Gaussianity is also a crucial assumption for identifying latent factors in ICA. We provide the first theoretical and empirical insights into this connection, showing that ICA can be used for causal effect estimation in the PLR model. Surprisingly, we find that linear ICA can accurately estimate multiple treatment effects even in the presence of Gaussian confounders or nonlinear nuisance.

Problem

• Estimating treatment effects using Independent Component Analysis
• Improving causal inference in partially linear regression models
• Addressing Gaussian confounders and nonlinear nuisance with ICA

Innovation

• Using ICA for causal effect estimation
• Leveraging non-Gaussian treatment noise
• Estimating effects despite Gaussian confounders