Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
This addresses the challenge of efficient and valid covariate selection for researchers in fields like epidemiology or social sciences, though it is incremental as it builds on existing causal inference methods.
The paper tackled the problem of covariate selection for causal effect estimation in the presence of latent variables by proposing a local learning approach, which achieved soundness and completeness under standard assumptions as validated through experiments on synthetic and real-world data.
Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science. A key component of this task is selecting an appropriate set of covariates for confounding adjustment to avoid bias. Most existing methods for covariate selection often assume the absence of latent variables and rely on learning the global network structure among variables. However, identifying the global structure can be unnecessary and inefficient, especially when our primary interest lies in estimating the effect of a treatment variable on an outcome variable. To address this limitation, we propose a novel local learning approach for covariate selection in nonparametric causal effect estimation, which accounts for the presence of latent variables. Our approach leverages testable independence and dependence relationships among observed variables to identify a valid adjustment set for a target causal relationship, ensuring both soundness and completeness under standard assumptions. We validate the effectiveness of our algorithm through extensive experiments on both synthetic and real-world data.