Causal discovery on high dimensional data

Existing causal discovery algorithms are usually not effective and efficient enough on high dimensional data. Because the high dimensionality reduces the discovered accuracy and increases the computation complexity. To alleviate these problems, we present a three-phase approach to learn the structure of nonlinear causal models by taking the advantage of feature selection method and two state of the art causal discovery methods. In the first phase, a greedy search method based on Max-Relevance and Min-Redundancy is employed to discover the candidate causal set, a rough skeleton of the causal network is generated accordingly. In the second phase, constraint-based method is explored to discover the accurate skeleton from the rough skeleton. In the third phase, direction learning algorithm IGCI is conducted to distinguish the direction of causalities from the accurate skeleton. The experimental results show that the proposed approach is both effective and scalable, particularly with interesting findings on the high dimensional data.