Causal Discovery from a Mixture of Experimental and Observational Data

TL;DR

This paper introduces a Bayesian approach to learn causal Bayesian networks from a mixture of observational and experimental data, demonstrating its effectiveness through experiments on the ALARM network.

Contribution

It presents a novel Bayesian method for integrating diverse data types to accurately learn causal structures and parameters in Bayesian networks.

Findings

The method effectively learns causal structures from mixed data.

Experimental data improves causal inference accuracy.

Performance varies with the ratio of observational to experimental data.

Abstract

This paper describes a Bayesian method for combining an arbitrary mixture of observational and experimental data in order to learn causal Bayesian networks. Observational data are passively observed. Experimental data, such as that produced by randomized controlled trials, result from the experimenter manipulating one or more variables (typically randomly) and observing the states of other variables. The paper presents a Bayesian method for learning the causal structure and parameters of the underlying causal process that is generating the data, given that (1) the data contains a mixture of observational and experimental case records, and (2) the causal process is modeled as a causal Bayesian network. This learning method was applied using as input various mixtures of experimental and observational data that were generated from the ALARM causal Bayesian network. In these experiments, the absolute and relative quantities of experimental and observational data were varied systematically. For each of these training datasets, the learning method was applied to predict the causal structure and to estimate the causal parameters that exist among randomly selected pairs of nodes in ALARM that are not confounded. The paper reports how these structure predictions and parameter estimates compare with the true causal structures and parameters as given by the ALARM network.