Statistical Science and Causal Discovery

I work on developing novel statistical methods to analyze data observed in social sciences, biomedical sciences, and natural sciences. The key is causal reasoning: Brief introduction and current research focus.

 Recent Highlights:

• NEW Special feature on recent developments in causal discovery and inference (Winter, 2017)
• Python code for mixed-LiNGAM (16 Nov 2016) 
• The first method capable of estimating the causal direction and magnitude between two variables in the presence of hidden common causes without explicitly modeling them (Shimizu & Bollen, 2014, JMLR)
• Talk slides at 2016 Munich Workshop on Causal Inference and Information Theory
  

 The LiNGAM Project:

This project develops semiparametric methods for learning mathematical models known as structural equation models. An important application of those methods is causal discovery.

• A short introduction
• NEW Introductory book: Causal Discovery (2017) (In Japanese)
• Introductory book chapters (Chapter 7 and Chapter 8. In Statistics and Causality, 2016)
• More introductory book chapter (In Japanese) (第4章. 確率的グラフィカルモデル, 2016)
• A survey paper (Behaviormetrika, 2014)
• Tutorials:
  • Tutorial slides at BSJ2012 and references (In Japanese)
  • Tutorial slides at UAI2010 and references
  • Talk slides at IMPS2008 and erratum
• A collection of LiNGAM-related papers