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Counterfactual Inference
Susan Athey

Mon Dec 03 11:30 AM -- 01:30 PM (PST) @ Room 517 CD

This tutorial will review the literature that brings together recent developments in machine learning with methods for counterfactual inference. It will focus on problems where the goal is to estimate the magnitude of causal effects, as well as to quantify the researcher’s uncertainty about these magnitudes. The tutorial will consider two strands of the literature. The first strand attempts to estimate causal effects of a single intervention, like a drug or a price change. The goal can be to estimate the average (counterfactual) effect of applying the treatment to everyone; or the conditional average treatment effect, which is the effect of applying the treatment to an individual conditional on covariates. We will also consider the problem of estimating an optimal treatment assignment policy (mapping features to assignments) under constraints on the nature of the policy, such as budget constraints. We look at applications to assigning unemployed workers to re-employment services. We finish by considering the case with multiple alternative treatments, as well as the link between this literature and the literature on contextual bandits. The second strand of the literature attempts to infer individual’s preferences from their behavior (inverse reinforcement learning in machine learning parlance, or structural estimation in econometrics parlance), and then predict an individual’s behavior in new environments. We look at applications to consumer choice behavior, and analyze counterfactuals around price changes. We discuss how models such as these can be tuned when the goal is counterfactual estimation rather than predicting outcomes.

Author Information

Susan Athey (Stanford University)

Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor's degree from Duke University and her Ph.D. from Stanford, and she holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT, Stanford and Harvard. In 2007, Professor Athey received the John Bates Clark Medal, awarded by the American Economic Association to “that American economist under the age of forty who is adjudged to have made the most significant contribution to economic thought and knowledge.” She was elected to the National Academy of Science in 2012 and to the American Academy of Arts and Sciences in 2008. Professor Athey’s research focuses on the economics of the internet, online advertising, the news media, marketplace design, and the intersection of machine learning and econometrics. She advises governments and businesses on marketplace design and platform economics.

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