A note on efficient minimum cost adjustment sets in causal graphical models

TL;DR

This paper introduces a method to identify cost-efficient adjustment sets in causal graphical models, optimizing for minimal cost and variance in estimating interventional means, using flow network algorithms.

Contribution

It proposes a novel approach to find minimum cost optimal adjustment sets via maximum flow algorithms in causal graphs, including implementation in a Python package.

Findings

Existence of minimum cost optimal adjustment sets.

Algorithm for finding these sets using maximum flow.

Implementation available in the 'optimaladj' Python package.

Abstract

We study the selection of adjustment sets for estimating the interventional mean under an individualized treatment rule. We assume a non-parametric causal graphical model with, possibly, hidden variables and at least one adjustment set comprised of observable variables. Moreover, we assume that observable variables have positive costs associated with them. We define the cost of an observable adjustment set as the sum of the costs of the variables that comprise it. We show that in this setting there exist adjustment sets that are minimum cost optimal, in the sense that they yield non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that control for observable adjustment sets that have minimum cost. Our results are based on the construction of a special flow network associated with the original causal graph. We show that a minimum cost optimal adjustment set can be found by computing a maximum flow on the network, and then finding the set of vertices that are reachable from the source by augmenting paths. The optimaladj Python package implements the algorithms introduced in this paper.