Separators and Adjustment Sets in Markov Equivalent DAGs

Authors

  • Benito van der Zander University of Luebeck
  • Maciej Liskiewicz University of Luebeck

DOI:

https://doi.org/10.1609/aaai.v30i1.10424

Keywords:

causality, causal effect, Markov equivalence classes, directed acyclic graph, chain graph

Abstract

In practice the vast majority of causal effect estimations from observational data are computed using adjustment sets which avoid confounding by adjusting for appropriate covariates. Recently several graphical criteria for selecting adjustment sets have been proposed. They handle causal directed acyclic graphs (DAGs) as well as more general types of graphs that represent Markov equivalence classes of DAGs, including completed partially directed acyclic graphs (CPDAGs). Though expressed in graphical language, it is not obvious how the criteria can be used to obtain effective algorithms for finding adjustment sets. In this paper we provide a new criterion which leads to an efficient algorithmic framework to find, test and enumerate covariate adjustments for chain graphs - mixed graphs representing in a compact way a broad range of Markov equivalence classes of DAGs.

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Published

2016-03-05

How to Cite

van der Zander, B., & Liskiewicz, M. (2016). Separators and Adjustment Sets in Markov Equivalent DAGs. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10424

Issue

Section

Technical Papers: Reasoning under Uncertainty