Discovering Markov Blanket from Multiple interventional Datasets

TL;DR

This paper introduces a novel algorithm for discovering the Markov blanket of a target variable from multiple interventional datasets, addressing challenges like unknown interventions and distribution differences, supported by theoretical analysis and empirical validation.

Contribution

It provides the first theoretical analysis and an effective algorithm for Markov blanket discovery across multiple interventional datasets, considering unknown interventions and data distribution variations.

Findings

The proposed algorithm accurately discovers Markov blankets in benchmark datasets.

Theoretical conditions ensure the correctness of the Markov blanket discovery method.

Experiments demonstrate the algorithm's efficiency and effectiveness on real-world data.

Abstract

In this paper, we study the problem of discovering the Markov blanket (MB) of a target variable from multiple interventional datasets. Datasets attained from interventional experiments contain richer causal information than passively observed data (observational data) for MB discovery. However, almost all existing MB discovery methods are designed for finding MBs from a single observational dataset. To identify MBs from multiple interventional datasets, we face two challenges: (1) unknown intervention variables; (2) nonidentical data distributions. To tackle the challenges, we theoretically analyze (a) under what conditions we can find the correct MB of a target variable, and (b) under what conditions we can identify the causes of the target variable via discovering its MB. Based on the theoretical analysis, we propose a new algorithm for discovering MBs from multiple interventional datasets, and present the conditions/assumptions which assure the correctness of the algorithm. To our knowledge, this work is the first to present the theoretical analyses about the conditions for MB discovery in multiple interventional datasets and the algorithm to find the MBs in relation to the conditions. Using benchmark Bayesian networks and real-world datasets, the experiments have validated the effectiveness and efficiency of the proposed algorithm in the paper.