# Causal Discovery with a Mixture of DAGs

## TL;DR

This paper introduces a novel approach to causal discovery using a mixture of directed cyclic graphs (DAGs) to model complex, evolving, and population-specific causal processes, with an algorithm leveraging longitudinal data for inference.

## Contribution

It proposes modeling causation with a mixture of DAGs and introduces the Causal Inference over Mixtures algorithm for longitudinal data analysis.

## Key findings

- Improved causal inference performance over prior methods.
- Effective modeling of cyclic, evolving, and population-specific causal processes.
- Algorithm successfully infers causal relations from complex data.

## Abstract

Causal processes in biomedicine may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose to model causation using a mixture of directed cyclic graphs (DAGs), where the joint distribution in a population follows a DAG at any single point in time but potentially different DAGs across time. We also introduce an algorithm called Causal Inference over Mixtures that uses longitudinal data to infer a graph summarizing the causal relations generated from a mixture of DAGs. Experiments demonstrate improved performance compared to prior approaches.