Complete identification methods for the causal hierarchy

Ilya Shpitser, Judea Pearl

Research output: Contribution to journalArticlepeer-review

Abstract

We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from a joint distribution over the observable variables; cause-effect relationships, derived from distributions resulting from external interventions; and counterfactuals, derived from distributions that span multiple "parallel worlds" and resulting from simultaneous, possibly conflicting observations and interventions. We completely characterize cases where a given causal query can be computed from information lower in the hierarchy, and provide algorithms that accomplish this computation. Specifically, we show when effects of interventions can be computed from observational studies, and when probabilities of counterfactuals can be computed from experimental studies. We also provide a graphical characterization of those queries which cannot be computed (by any method) from queries at a lower layer of the hierarchy.

Original languageEnglish (US)
Pages (from-to)1941-1979
Number of pages39
JournalJournal of Machine Learning Research
Volume9
StatePublished - Sep 1 2008

Keywords

  • Causality
  • Graphical causal models
  • Identification

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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