On Learning Necessary and Sufficient Causal Graphs

TL;DR

This paper introduces a novel approach for learning causal graphs that focus only on variables relevant to a specific outcome, improving accuracy and interpretability in causal inference tasks.

Contribution

It proposes the NSCG framework and NSCSL algorithm, which identify causally relevant features using probabilities of causation, advancing causal structure learning.

Findings

NSCSL outperforms existing algorithms in simulated and real data.

The method successfully identifies key causal variables like yeast genes.

The approach reduces false discoveries by focusing on relevant causal features.

Abstract

The causal revolution has stimulated interest in understanding complex relationships in various fields. Most of the existing methods aim to discover causal relationships among all variables within a complex large-scale graph. However, in practice, only a small subset of variables in the graph are relevant to the outcomes of interest. Consequently, causal estimation with the full causal graph -- particularly given limited data -- could lead to numerous falsely discovered, spurious variables that exhibit high correlation with, but exert no causal impact on, the target outcome. In this paper, we propose learning a class of necessary and sufficient causal graphs (NSCG) that exclusively comprises causally relevant variables for an outcome of interest, which we term causal features. The key idea is to employ probabilities of causation to systematically evaluate the importance of features in the causal graph, allowing us to identify a subgraph relevant to the outcome of interest. To learn NSCG from data, we develop a necessary and sufficient causal structural learning (NSCSL) algorithm, by establishing theoretical properties and relationships between probabilities of causation and natural causal effects of features. Across empirical studies of simulated and real data, we demonstrate that NSCSL outperforms existing algorithms and can reveal crucial yeast genes for target heritable traits of interest.