I am working in the area of causal inference, specifically inferring causal relations in case where only observational data is available. My interests include identifying finite latent common causes of some observed variables based on their joint probability distribution. I am part of the Causal Inference Group.
In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (inproceedings)
In pages: 383-391, (Editors: FG Cozman and A Pfeffer), AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI), July 2011 (inproceedings)
We describe a method that infers whether statistical dependences between two observed variables X and Y are due to a \direct" causal link or only due to a connecting causal
path that contains an unobserved variable of low complexity, e.g., a binary variable. This problem is motivated by statistical genetics. Given a genetic marker that is correlated with a phenotype of interest, we want to
detect whether this marker is causal or it only correlates with a causal one. Our method is based on the analysis of the location of the conditional distributions P(Y jx) in the simplex of all distributions of Y . We report encouraging results on semi-empirical data.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems