Causal reasoning in difference graphs

TL;DR

This paper develops methods for analyzing causal differences between populations using difference graphs, enabling better understanding of causal variations and effects in public health contexts.

Contribution

It establishes conditions for identifying causal changes and effects from difference graphs in both nonparametric and linear settings, advancing causal reasoning techniques.

Findings

Conditions for identifying causal changes and effects

Methods for nonparametric and linear causal inference

Potential applications in public health interventions

Abstract

Understanding causal mechanisms across different populations is essential for designing effective public health interventions. Recently, difference graphs have been introduced as a tool to visually represent causal variations between two distinct populations. While there has been progress in inferring these graphs from data through causal discovery methods, there remains a gap in systematically leveraging their potential to enhance causal reasoning. This paper addresses that gap by establishing conditions for identifying causal changes and effects using difference graphs. It specifically focuses on identifying total causal changes and total effects in a nonparametric setting, as well as direct causal changes and direct effects in a linear setting. In doing so, it provides a novel approach to causal reasoning that holds potential for various public health applications.