Finding and Listing Front-door Adjustment Sets

TL;DR

This paper develops algorithms to identify and enumerate sets satisfying Pearl's front-door criterion in causal diagrams, aiding causal effect estimation and experimental design.

Contribution

It introduces algorithms for finding and listing all sets that satisfy the front-door criterion in a causal diagram, enhancing practical causal analysis.

Findings

Algorithms successfully identify front-door sets in various diagrams.

Facilitates selection of estimands based on cost, feasibility, or power.

Supports practical application of the front-door criterion.

Abstract

Identifying the effects of new interventions from data is a significant challenge found across a wide range of the empirical sciences. A well-known strategy for identifying such effects is Pearl's front-door (FD) criterion (Pearl, 1995). The definition of the FD criterion is declarative, only allowing one to decide whether a specific set satisfies the criterion. In this paper, we present algorithms for finding and enumerating possible sets satisfying the FD criterion in a given causal diagram. These results are useful in facilitating the practical applications of the FD criterion for causal effects estimation and helping scientists to select estimands with desired properties, e.g., based on cost, feasibility of measurement, or statistical power.