Tutorial Session B – Causes and Counterfactuals: Concepts, Principles and Tools.


January 6, 2014


The traditional aim of machine learning methods is to infer meaningful features of an underlying probability distribution from samples drawn of that distribution. With the help of such features, one can infer associations of interest and predict or classify yet unobserved samples. Causal analysis goes one step further; it aims at inferring features of the data-generating process, that is, of the invariant strategy by which Nature assigns values to the variables in the distribution. Process features enable us to predict, not merely relationships governed by the underlying distribution, but also how that distribution would CHANGE when conditions are altered, say, by deliberate interventions or by spontaneous transformations.

We will review concepts, principles, and mathematical tools that were found useful in reasoning about causal and counterfactual relations, and will demonstrate their applications in several data-intensive sciences. These include questions of confounding control, policy analysis, misspecification tests, mediation, heterogeneity, selection bias, missing data, and the integration of findings from diverse studies.


Judea Pearl and Elias Bareinboim

Judea Pearl is a professor of computer science and statistics at UCLA. He is a graduate of the Technion, Israel, and has joined the faculty of UCLA in 1970, where he conducts research in artificial intelligence, causal inference and philosophy of science. Pearl has authored three books: Heuristics (1984), Probabilistic Reasoning (1988), and Causality (2000;2009), the latter won the Lakatos Prize from the London School of Economics. He is a member of the National Academy of Engineering, the American Academy of Arts and Sciences, and a Fellow of the IEEE, AAAI and the Cognitive Science Society. Pearl received the 2008 Benjamin Franklin Medal from the Franklin Institute and the 2011 Rumelhart Prize from the Cognitive Science Society. In 2012, he received the Technion’s Harvey Prize and the ACM Alan M. Turing Award.

Elias Bareinboim is a PhD candidate in Computer Science at UCLA advised by Judea Pearl. He works on the problem of generalizability in causal inference, and more specifically proposed solutions for the problems of selection bias, fusion of experimental and non-experimental knowledge, and external validity (transfer of causal knowledge) in non-parametric settings. Recently, Elias received the “Yahoo Key Scientific Challenges Award 2012” (area of Statistics) and Dissertation Year Fellowship (2013-2014) from UCLA. He holds B.Sc. and M.Sc. degrees in Computer Science from Federal University of Rio de Janeiro, Brazil, where he worked in the areas of Complex Networks, Artificial Intelligence, and Bioinformatics.