Discovering Reliable Causal Rules

TL;DR

This paper introduces a method for discovering reliable causal rules from observational data by adjusting for confounders and providing an efficient algorithm that outperforms naive estimators, with proven convergence and effectiveness.

Contribution

It presents a novel causal effect estimator, a graphical criterion for causal rule discovery, and an efficient algorithm to reliably identify causal rules from observational data.

Findings

Estimator converges faster than naive methods

Algorithm efficiently discovers meaningful causal rules

Effective on both synthetic and real-world datasets

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 can lead to random results. To address these issues, first we measure the causal effect of a rule from observational data---adjusting for the effect of potential confounders. Importantly, we provide a graphical criteria 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. On synthetic data, the proposed estimator converges faster to the ground truth than the naive estimator and recovers relevant causal rules even at small sample sizes. Extensive experiments on a variety of real-world datasets show that the proposed algorithm is efficient and discovers meaningful rules.