SADA: A General Framework to Support Robust Causation Discovery

Causality discovery without manipulation is considered a crucial problem to a variety of applications, such as genetic therapy. The state-of-the-art solutions, e.g. LiNGAM, return accurate results when the number of labeled samples is larger than the number of variables. These approaches are thus applicable only when large numbers of samples are available or the problem domain is sufficiently small. Motivated by the observations of the local sparsity properties on causal structures, we propose a general Split-andMerge strategy, named SADA, to enhance the scalability of a wide class of causality discovery algorithms. SADA is able to accurately identify the causal variables, even when the sample size is significantly smaller than the number of variables. In SADA, the variables are partitioned into subsets, by finding cuts on the sparse probabilistic graphical model over the variables. By running mainstream causation discovery algorithms, e.g. LiNGAM, on the subproblems, complete causality can be reconstructed by combining all the partial results. SADA benefits from the recursive division technique, since each small subproblem generates more accurate result under the same number of samples. We theoretically prove that SADA always reduces the scale of problems withProceedings of the 30 th International Conference on Machine Learning, Atlanta, Georgia, USA, 2013. JMLR: W&CP volume 28. Copyright 2013 by the author(s). out significant sacrifice on result accuracy, depending only on the local sparsity condition over the variables. Experiments on realworld datasets verify the improvements on scalability and accuracy by applying SADA on top of existing causation algorithms.