Presented By: School of Information
Making Good Policies with Bad Causal Inference: The Role of Prediction and Machine Learning
Sendhil Mullainathan, Professor of Economics, Harvard University
Yahoo/SBEE (Social, Behavioral, and Experimental Economics) Seminar
In the last few decades, we have learned to be careful about causation, and have developed powerful tools for making causal inferences from data. Applying these tools has generated both policy impact and conceptual insights. Dr. Mullainathan will argue that there are a large class of problems where causal inference is largely unnecessary where, instead, prediction is the central challenge. These problems are ideally suited to machine learning and high dimensional data analysis tools. In this talk Dr. Mullainathan will (1) try to delineate the difference between problems that require causation and problems that require prediction; (2) describe results from solving one such prediction problem in detail; (3) highlight the set of new statistical issues these problems raise; and (4) argue that solving these problems can also generate both policy impact and conceptual insights.
Sendhil Mullainathan is a Professor of Economics at Harvard University. His real passion is behavioral economics. His work runs a wide gamut: the impact of poverty on mental bandwidth; whether CEO pay is excessive; using fictitious resumes to measure discrimination; showing that higher cigarette taxes makes smokers happier; modeling how competition affects media bias; and a model of coarse thinking. His latest research focuses on using machine learning and data mining techniques to better understand human behavior.
He recently co-authored "Scarcity: Why Having Too Little Means So Much" and writes regularly for the New York Times. He is the recipient of a MacArthur "genius grant" and serves on the board of the MacArthur Foundation.
In the last few decades, we have learned to be careful about causation, and have developed powerful tools for making causal inferences from data. Applying these tools has generated both policy impact and conceptual insights. Dr. Mullainathan will argue that there are a large class of problems where causal inference is largely unnecessary where, instead, prediction is the central challenge. These problems are ideally suited to machine learning and high dimensional data analysis tools. In this talk Dr. Mullainathan will (1) try to delineate the difference between problems that require causation and problems that require prediction; (2) describe results from solving one such prediction problem in detail; (3) highlight the set of new statistical issues these problems raise; and (4) argue that solving these problems can also generate both policy impact and conceptual insights.
Sendhil Mullainathan is a Professor of Economics at Harvard University. His real passion is behavioral economics. His work runs a wide gamut: the impact of poverty on mental bandwidth; whether CEO pay is excessive; using fictitious resumes to measure discrimination; showing that higher cigarette taxes makes smokers happier; modeling how competition affects media bias; and a model of coarse thinking. His latest research focuses on using machine learning and data mining techniques to better understand human behavior.
He recently co-authored "Scarcity: Why Having Too Little Means So Much" and writes regularly for the New York Times. He is the recipient of a MacArthur "genius grant" and serves on the board of the MacArthur Foundation.
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