  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.
