Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions

2026-05-20

AI Summary

This study addresses the problem of covariate selection for unbiased estimation of average causal effects without requiring preprocessing variables or the assumption of causal sufficiency. To this end, the authors propose a local learning method that characterizes a local boundary containing valid adjustment sets and efficiently searches within it to enable nonparametric causal effect estimation. This approach circumvents conventional reliance on a complete causal graph, causal sufficiency, and pre-specified variable preprocessing, thereby achieving local covariate selection under substantially weaker assumptions than prior methods. Empirical evaluations demonstrate that the proposed method accurately estimates causal effects across multiple synthetic datasets as well as two real-world datasets, while exhibiting significantly higher computational efficiency compared to existing approaches.

Abstract

We study the problem of selecting covariates for unbiased estimation of the total causal effect.Existing approaches typically rely on global causal structure learning over all variables, or on strong assumptions such as causal sufficiency - where observed variables share no latent confounders - or the pretreatment assumption, which limits covariates to those unaffected by the treatment or outcome. These requirements are often unrealistic in practice, and global learning becomes computationally prohibitive in high-dimensional settings.To address these challenges, we propose a novel local learning method for covariate selection in nonparametric causal effect estimation that avoids both the pretreatment and causal sufficiency assumptions. We first characterize a local boundary that contains at least one valid adjustment set whenever one exists for identifying the causal effect, and then develop local identification procedures to efficiently search within this boundary.We prove that the proposed method is sound and complete. Experiments on multiple synthetic datasets and two real-world datasets show that our approach achieves accurate causal effect estimation while substantially improving computational efficiency.

Problem

• covariate selection
• average causal effect
• causal sufficiency
• pretreatment assumption
• unbiased estimation

Innovation

• local covariate selection
• causal effect estimation
• nonparametric
• adjustment set
• causal discovery