Abstract: 

Causal inference is increasingly an indispensable tool of data science, machine learning, and data-driven decision-making. In this talk I will present the state-of-play in causal machine learning. I cover the problems that matter in practice, with emphasis on the tech and retail industries. I will also talk about trends in opensource tools for causal inference. Finally, I'll show examples from DoWhy and its sister package EconML, which together form the PyTorch of causal inference.

Bio: 

Robert Osazuwa Ness is a researcher at Microsoft Research and author of the book Causal Machine Learning. He leads the development of MSR’s causal machine learning platform and conducts research into probabilistic models for advanced causal reasoning. He has worked as a machine learning engineer in various machine learning startups. He attended graduate school at both Johns Hopkins SAIS (Hopkins-Nanjing Center) and Purdue University. He received his Ph.D. in Statistics from Purdue, where his dissertation research focused on Bayesian active learning models for causal discovery.