Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structure

TL;DR

This paper introduces a causal discovery algorithm that leverages temporal background knowledge to improve robustness and accuracy in finite samples, especially useful for biomedical data.

Contribution

The paper presents a novel algorithm that exploits tiered temporal background knowledge to enhance causal discovery's stability and accuracy in finite samples.

Findings

Algorithm improves robustness to statistical errors.

Demonstrates increased accuracy in simulations.

Effective application to real cohort data.

Abstract

Methods of causal discovery aim to identify causal structures in a data driven way. Existing algorithms are known to be unstable and sensitive to statistical errors, and are therefore rarely used with biomedical or epidemiological data. We present an algorithm that efficiently exploits temporal structure, so-called tiered background knowledge, for estimating causal structures. Tiered background knowledge is readily available from, e.g., cohort or registry data. When used efficiently it renders the algorithm more robust to statistical errors and ultimately increases accuracy in finite samples. We describe the algorithm and illustrate how it proceeds. Moreover, we offer formal proofs as well as examples of desirable properties of the algorithm, which we demonstrate empirically in an extensive simulation study. To illustrate its usefulness in practice, we apply the algorithm to data from a children's cohort study investigating the interplay of diet, physical activity and other lifestyle factors for health outcomes.