Discovering reliable causal rules

Kailash Budhathoki, Mario Boley, Jilles Vreeken

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 publicationSIAM International Conference on Data Mining, SDM 2021
PublisherSiam Society
Pages1-9
Number of pages9
ISBN (Electronic)9781611976700
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 SIAM International Conference on Data Mining, SDM 2021 - Virtual, Online
Duration: 29 Apr 20211 May 2021

Publication series

NameSIAM International Conference on Data Mining, SDM 2021

Conference

Conference2021 SIAM International Conference on Data Mining, SDM 2021
CityVirtual, Online
Period29/04/211/05/21

Bibliographical note

Publisher Copyright:
© 2021 by SIAM.

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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