Presented By: School of Information
SBEE lecture: Making Good Policies with Bad Causal Inference: The Role of Prediction and Machine Learning
Sendhil Mullainathan, Harvard University
As part of the School of Information’s Yahoo! seminar series and Social, Behavioral and Experimental Economics (SBEE) lecture series, Sendhil Mullainathan, Professor of Economics at Harvard University, will discuss his recent research on the intersection of behavioral economics and machine learning. This talk is sponsored by the School of Information, Center on Finance, Law, and Policy, and Social, Behavioral, and Experimental Economics seminar. Lunch will be provided.
<strong>Making Good Policies with Bad Causal Inference: The Role of Prediction and Machine Learning</strong>
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.
<strong>About the speaker:</strong>
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 enjoys writing, having recently co-authored Scarcity: Why Having too Little Means so Much and writes regularly for the New York Times.
He helped co-found a non-profit to apply behavioral science (ideas42), co-founded a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), serves on the board of the MacArthur Foundation, and has worked in government in various roles, including most recently as Assistant Director of Research at the Consumer Financial Protection Bureau.
He is a recipient of the MacArthur “genius” Award, has been designated a “Young Global Leader” by the World Economic Forum, labeled a “Top 100 Thinker” by Foreign Policy Magazine, and named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK). His hobbies include basketball, board games, googling and fixing-up classic espresso machines.
<strong>Making Good Policies with Bad Causal Inference: The Role of Prediction and Machine Learning</strong>
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.
<strong>About the speaker:</strong>
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 enjoys writing, having recently co-authored Scarcity: Why Having too Little Means so Much and writes regularly for the New York Times.
He helped co-found a non-profit to apply behavioral science (ideas42), co-founded a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), serves on the board of the MacArthur Foundation, and has worked in government in various roles, including most recently as Assistant Director of Research at the Consumer Financial Protection Bureau.
He is a recipient of the MacArthur “genius” Award, has been designated a “Young Global Leader” by the World Economic Forum, labeled a “Top 100 Thinker” by Foreign Policy Magazine, and named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK). His hobbies include basketball, board games, googling and fixing-up classic espresso machines.
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