Discovering reliable causal rules

Kailash Budhathoki, Mario Boley, Jilles Vreeken

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

We study the problem of deriving policies, or rules, that when enacted on a complex system, cause a desired outcome. Absent the ability to perform controlled experiments, such rules have to be inferred from past observations of the system's behaviour. This is a challenging problem for two reasons: First, observational effects are often unrepresentative of the underlying causal effect because they are skewed by the presence of confounding factors. Second, naive empirical estimations of a rule's effect have a high variance, and, hence, their maximisation typically leads to spurious results.

To address these issues, we first identify conditions on the underlying causal system that—by correcting for the effect of potential confounders—allow estimating the causal effect from observational data. Importantly, we provide a criterion under which causal rule discovery is possible. Moreover, to discover reliable causal rules from a sample, we propose a conservative and consistent estimator of the causal effect, and derive an efficient and exact algorithm that maximises the estimator. Extensive experiments on a variety of real-world and synthetic datasets show that the proposed estimator converges faster to the ground truth than the naive estimator, recovers causal rules even at small sample sizes, and the proposed algorithm efficiently discovers meaningful rules.
Original languageEnglish
Title of host publicationProceedings of the 2021 SIAM International Conference on Data Mining (SDM)
EditorsCarlotta Demeniconi, Ian Davidson
Place of PublicationPhiladephia PA USA
PublisherSociety for Industrial & Applied Mathematics (SIAM)
Pages1-9
Number of pages9
ISBN (Electronic)9781611976700
DOIs
Publication statusPublished - 2021
EventSIAM International Conference on Data Mining 2021 - Online, United States of America
Duration: 29 Apr 20211 May 2021
https://epubs.siam.org/doi/book/10.1137/1.9781611976700 (Proceedings)
https://www.siam.org/conferences/cm/conference/sdm21 (Website)

Conference

ConferenceSIAM International Conference on Data Mining 2021
Abbreviated titleSDM21
Country/TerritoryUnited States of America
Period29/04/211/05/21
Internet address

Keywords

  • rule learning
  • branch-and-bound
  • subgroup discovery
  • causal analysis

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