Causal Discovery with a Mixture of DAGs

2019/01/28 by Eric V. Strobl, Strobl, Eric V.
Computer Science · Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.AP #stat.ML

paper · pdf · doi:10.48550/arxiv.1901.09475

arxiv created 2020/09/05 · arxiv updated 2020/09/08

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.

Related

• Interventional Causal Discovery in a Mixture of DAGs
• Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
• From Causal Pairs to Causal Graphs
• The Global Markov Property for a Mixture of DAGs
• Estimating causal structure using conditional DAG models