As a basic tool for explanation, prediction and decision making, causal inference has been utilized in almost all disciplines. Traditionally, causal relationships are identified based on controlled experiments. However, conducting such experiments is impossible in many cases due to cost or ethical concerns. Therefore there has been an increasing interest in discovering causal relationships from observational data only. Recently with the rapid accumulation of huge volume of data, the field of causal discovery is seeing exciting opportunities, as well as greater challenges. This workshop is aimed at bringing together researchers and practitioners with the interest in causal discovery, from data mining and other disciplines, to communicate their new ideas, algorithms, and novel applications of causal discovery methods. The workshop especially welcomes contributions that link data mining research with causal discovery, and solutions to causal discovery from large scale data sets.