Finding and Listing Front-door Adjustment Sets
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.
Cite
@article{arxiv.2210.05816,
title = {Finding and Listing Front-door Adjustment Sets},
author = {Hyunchai Jeong and Jin Tian and Elias Bareinboim},
journal= {arXiv preprint arXiv:2210.05816},
year = {2022}
}
Comments
Pages: 18 (main paper 10, references 2, appendix 6), Figures: 9 (main paper 7, appendix 2), to be published in Proceedings of the 36th Annual Conference on Neural Information Processing Systems