The Variance of Causal Effect Estimators for Binary V-structures

TL;DR

This paper analyzes the variance of causal effect estimators in binary V-structures, revealing that the optimal adjustment set depends on model parameters and sample size, impacting practical causal inference strategies.

Contribution

It explicitly compares variances of different estimators in a binary V-structure and shows parameter-dependent optimal adjustment set selection beyond asymptotic approximations.

Findings

Variance of estimators varies with model parameters.

Optimal adjustment set depends on edge coefficients and sample size.

Graphical criteria alone are insufficient for adjustment set choice.

Abstract

Adjusting for covariates is a well established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study there may be different adjustment sets, equally valid from a theoretical perspective, leading to identical causal effects. However, in practice, with finite data, estimators built on different sets may display different precision. To investigate the extent of this variability we consider the simplest non-trivial non-linear model of a v-structure on three nodes for binary data. We explicitly compute and compare the variance of the two possible different causal estimators. Further, by going beyond leading order asymptotics we show that there are parameter regimes where the set with the asymptotically optimal variance does depend on the edge coefficients, a result which is not captured by the recent leading order developments for general causal models. As a practical consequence, the adjustment set selection needs to account for the relative magnitude of the relationships between variables with respect to the sample size, and cannot rely on purely graphical criteria.