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.
History
Preferred Citation
Kailash Budhathoki, Mario Boley and Jilles Vreeken. Rule Discovery for Exploratory Causal Reasoning. In: Conference on Neural Information Processing Systems (NeurIPS). 2018.
Primary Research Area
Empirical and Behavioral Security
Name of Conference
Conference on Neural Information Processing Systems (NeurIPS)
Legacy Posted Date
2019-06-07
Open Access Type
Unknown
BibTeX
@inproceedings{cispa_all_2906,
title = "Rule Discovery for Exploratory Causal Reasoning",
author = "Budhathoki, Kailash and Boley, Mario and Vreeken, Jilles",
booktitle="{Conference on Neural Information Processing Systems (NeurIPS)}",
year="2018",
}