Causal Discovery via Statistical Power (CDSP)

TL;DR

CDSP is a novel statistical inference framework for causal discovery that quantifies uncertainty and improves accuracy by leveraging effect-size asymmetries in observational data.

Contribution

It introduces a new effect-size asymmetry assumption and connects causal direction estimation with statistical power, enabling uncertainty quantification in causal discovery.

Findings

CDSP reduces false discovery rates by approximately 18% on benchmark data.

The method is robust to mild and moderate model misspecifications.

It effectively determines causal direction using effect-size asymmetries.

Abstract

Causal discovery methods aim to infer causal direction from observational data. Functional causal discovery approaches use structural asymmetries to identify causal directionality but rely on strong modeling assumptions and provide limited tools for uncertainty quantification. We introduce Causal Discovery via Statistical Power (CDSP), a statistical inference framework that connects causal direction estimation with statistical power and enables uncertainty quantification. Considering the foundational setting of bivariate observational data, we show how quantities analogous to statistical power and effect size can be used in causal discovery to determine when data contain sufficient information to favor one direction over the other. We introduce the effect-size asymmetry assumption that characterizes when the probability of correctly detecting the causal direction (i.e., the power of causal discovery) exceeds that of incorrectly favoring the reverse direction. We show that the effect-size asymmetry assumption can be used for causal direction estimation with uncertainty quantification. Simulations show that CDSP direction estimation is robust to mild and moderate model misspecifications. Real data analyses on 100 cause-effect benchmark pairs further demonstrate that CDSP reduces false discovery rates by approximately 18% relative to a commonly used existing method.