Abstract
I will discuss two basic algorithmic problems in statistical and causal inference. The first problem is to approximate the distance between two Bayesian networks. The second problem is to approximately learn an interventional distribution from observational samples. For both problems, assuming that all variables are over a finite domain, I will describe efficient algorithms with finite sample guarantees as well as computational hardness for various parameter regimes. Joint work with Pavan Aduri, Sutanu Gayen, Kuldeep Meel, Dimitrios Myrisiotis, and NV Vinodchandran.