Finding and Listing Front-door Adjustment Sets
This work addresses the challenge of causal inference for scientists by providing tools to facilitate the use of the front-door criterion, though it is incremental as it builds on existing declarative definitions.
The paper tackles the problem of identifying causal effects from data by developing algorithms to find and list all sets that satisfy Pearl's front-door criterion in a given causal diagram, enabling practical applications like selecting estimands based on cost or feasibility.
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.