Causal discovery with a mixture of DAGs

Eric V. Strobl

Abstract: Real causal processes 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); each sample follows a joint distribution that factorizes according to a DAG, but the DAG may differ between samples due to multiple independent factors. We then 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 even when cycles, non-stationarity, latent variables or selection bias exist. Experiments demonstrate improved performance in inferring ancestral relations as compared to prior approaches. R code is available at https://github.com/ericstrobl/CIM.