From Causal Pairs to Causal Graphs

TL;DR

This paper introduces a probabilistic approach to causal graph learning from observational data, leveraging cause-effect pair features to improve efficiency and performance over traditional methods.

Contribution

It proposes new methods that generate a probability distribution over causal graphs using cause-effect pair features, enhancing both speed and accuracy.

Findings

Methods are statistically comparable or superior to traditional approaches.

Proposed methods are computationally faster.

Effective on both synthetic and real datasets.

Abstract

Causal structure learning from observational data remains a non-trivial task due to various factors such as finite sampling, unobserved confounding factors, and measurement errors. Constraint-based and score-based methods tend to suffer from high computational complexity due to the combinatorial nature of estimating the directed acyclic graph (DAG). Motivated by the `Cause-Effect Pair' NIPS 2013 Workshop on Causality Challenge, in this paper, we take a different approach and generate a probability distribution over all possible graphs informed by the cause-effect pair features proposed in response to the workshop challenge. The goal of the paper is to propose new methods based on this probabilistic information and compare their performance with traditional and state-of-the-art approaches. Our experiments, on both synthetic and real datasets, show that our proposed methods not only have statistically similar or better performances than some traditional approaches but also are computationally faster.