Causal Structure–Informed Covariate Adjustment for Debiased Effect Estimation

Thomas M. Menino Convention & Exhibition Center 

Causal effect estimation requires principled covariate adjustment to avoid bias induced by inappropriate conditioning. However, in practice, this task is challenging due to inadvertent collider adjustment and linear confounding violation. To address these, we propose a two-step approach that integrates causal structure learning with debiased estimation. In the first step, we perform structural equation modeling (SEM) to detect a directed acyclic graph (DAG) among outcome, exposure and covariates. This allows us to distinguish confounders from colliders based on the inferred causal structure, thus yielding an adjustment set. In the second step, we employ double machine learning (DML) to model confounding relationships and debias the causal effect estimation with the confounding selection in Step 1. The proposed framework is capable of handling colliders through DAG-based structure learning and accommodating nonlinear relationships via nonlinear SEM in the DAG discovery phase and multilayer perceptron in DML. We illustrate the finite-sample performance of this method through extensive simulation and the real-world data from a community-based cohort Framingham Heart Study.

Causal inference

Observational study

Double machine learning

Causal structure learning

Variable selection 

Section on Statistics in Epidemiology