Graphical tools for selecting conditional instrumental sets

TL;DR

This paper introduces graphical tools for selecting efficient conditional instrumental sets to improve causal effect estimation in linear models with unmeasured confounding.

Contribution

It characterizes valid conditional instrumental sets, derives a new asymptotic variance formula, and provides three graphical tools for optimal set selection.

Findings

Graphical criterion for comparing estimator variances

Algorithm for greedy covariate addition

Identification of minimal variance instrumental sets

Abstract

We consider the efficient estimation of total causal effects in the presence of unmeasured confounding using conditional instrumental sets. Specifically, we consider the two-stage least squares estimator in the setting of a linear structural equation model with correlated errors that is compatible with a known acyclic directed mixed graph. To set the stage for our results, we characterize the class of linearly valid conditional instrumental sets that yield consistent two-stage least squares estimators for the target total effect and derive a new asymptotic variance formula for these estimators. Equipped with these results, we provide three graphical tools for selecting more efficient linearly valid conditional instrumental sets. First, a graphical criterion that for certain pairs of linearly valid conditional instrumental sets identifies which of the two corresponding estimators has the smaller asymptotic variance. Second, an algorithm that greedily adds covariates that reduce the asymptotic variance to a given linearly valid conditional instrumental set. Third, a linearly valid conditional instrumental set for which the corresponding estimator has the smallest asymptotic variance that can be ensured with a graphical criterion.