Learning Causal AMP Chain Graphs

Andersson-Madigan-Perlman chain graphs were originally introduced to represent independence models. They have recently been shown to be suitable for representing causal models with additive noise. In this paper, we present an algorithm for learning causal chain graphs. The algorithm builds on the ideas by Hoyer et al. (2009), i.e. it exploits the nonlinearities in the data to identify the direction of the causal relationships. We also report experimental results on real-world data.