Partial Correlation-and Regression-Based Approaches to Causal Structure Learning Technical Report

  title={Partial Correlation-and Regression-Based Approaches to Causal Structure Learning Technical Report},
  author={Jean-Philippe Pellet and Andr{\'e} Elisseeff},
We present the Total Conditioning (TC) algorithm for causal discovery suited in the presence of continuous variables. Given a set of n data points drawn from a distribution whose underlying causal structure is a directed acyclic graph (DAG), the TC algorithm returns a structure, i.e., a DAG, over the variables that tends to the correct structure when n tends to infinity. The approach builds on the structural equation modeling framework, well suited for continuous variables, and relies on causal… CONTINUE READING
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