Rule discovery for exploratory causal reasoning

Kailash Budhathoki, Mario Boley, Jilles Vreeken

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch


We study the problem of discovering reliable causal rules from observational data. Traditional descriptive rule discovery techniques do not suffice to this end, as they struggle with the consistent detection of (potentially rare) conditions that have a strong effect on an output variable of interest. Among the sources of inconsistency are that naive empirical effect estimations have a high variance, and, hence, their maximization is highly optimistically biased unless the search is artificially restricted to high frequency events. Secondly, observational effect measurements are often highly unrepresentative of the underlying causal effect because they are skewed by the presence of confounding factors. This is a concern especially in scientific data analysis.

To address these issues, we present a novel descriptive rule discovery approach based on reliably estimating the conditional effect given the potential confounders. We demonstrate that the corresponding score is a conservative and consistent effect estimator, identify the admissible data generation process under which causal rule discovery is possible, and derive an efficient optimization algorithm that successfully detects valuable rules on a multitude of real datasets. Important for both causal and associational data exploration, the presented approach naturally allows for iterative rule discovery, where new non-redundant rules can be found by treating previously discovered rules as confounders in subsequent iterations.
Original languageEnglish
Title of host publicationNeurIPS 2018 Workshop on Causal Learning
EditorsMartin Arjovsky, Christina Heinze-Deml, Anna Klimovskaia, Maxime Oquab, Leon Bottou, David Lopez-Paz
PublisherNeural Information Processing Systems Foundation Inc.
Number of pages14
Publication statusPublished - 2018
EventNeurIPS 2018 Workshop on Causal Learning - Montreal , Canada
Duration: 7 Dec 20187 Dec 2018


ConferenceNeurIPS 2018 Workshop on Causal Learning
Abbreviated titleNeurIPS 2018
Internet address

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