B0890
Title: Non-Gaussian methods for causal discovery
Authors: Shohei Shimizu - Shiga University (Japan) [presenting]
Abstract: Statistical causal inference is a methodology that combines domain knowledge and data to support decision-making based on understanding causal mechanisms. A central problem in science is to elucidate the causal mechanisms underlying natural phenomena and human behavior. Statistical causal inference offers various tools to study such mechanisms. However, due to a lack of background knowledge, preparing causal graphs required for performing statistical causal inference is often difficult. To alleviate this difficulty, a lot of work has been conducted to develop statistical methods for estimating causal relationships, i.e., the causal structure of variables, from observational data obtained from sources other than randomized experiments. Statistical causal discovery is such a methodology that uses data to infer the causal structure of variables. The purpose is to outline the basic ideas and typical approaches of statistical causal discovery to introduce some recent advances in the field. In particular, the focus is on methods based on non-Gaussianity that can handle unobserved variables.
