Learning causal Bayesian networks based on causality analysis for classification

Revealing causal information by analyzing purely observational data, known as causal discovery, has drawn much attention. To prove that the causal knowledge mined from data can be applied to facilitate various machine learning tasks (e.g., classification), we propose to measure, describe and evaluate the causalities in the framework of Bayesian network (BN) learning. In this paper, heuristic search strategy is applied to explore the causal interpretation in the form of directed acyclic graph (DAG) for classification. While adding directed edges to the DAG, we first introduce the log-likelihood equivalence assertion to make the learned joint probability encoded in BN approximates the true one, then introduce the causal dependence assertion to assess the rationality of the learned causal relationship. We perform a range of experiments on 35 datasets and empirically show that this novel algorithm demonstrates competitive classification performance and excellent causal interpretation compared to state-of-the-art Bayesian network classifiers (e.g. SKDB, WATAN, SLB, and TAODE).

Automated summary

This paper introduces a method to measure, describe, and evaluate causal relationships within the framework of Bayesian network learning, and empirically demonstrates its competitive performance in classification and causal interpretation.