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Mixtape Session: Empirical Bayes Methods
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Instructor:
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 Christopher Walters, University of California – Berkeley
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Email:
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 crwalters@econ.berkeley.edu
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Personal website:
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 https://eml.berkeley.edu/~crwalters/
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Course website: 
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All materials will be available on a Github repo at https://github.com/Mixtape-Se
ssions/
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Description
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Applied microeconomic research increasingly focuses on applications involving
 large numbers of unit-specific parameters.
 Examples include studies of school, teacher, and physician quality; neighborhoo
d effects on economic mobility; firm effects on wages; employer-specific
 labor market discrimination; and individualized treatment effect predictions
 and policy recommendations.
 Empirical Bayes methods offer powerful tools for summarizing heterogeneity,
 estimating individual effects, and making decisions in these settings.
 This session will cover theory and applications of empirical Bayes methods,
 with an emphasis on developing skills to deploy these methods in realistic
 applications.
 Topics will include methods for quantifying variation in effects, empirical
 Bayes shrinkage, connections to machine learning methods, and large-scale
 inference tools for multiple testing and decision-making.
 By the end of the course, participants will be equipped to utilize these
 methods in their own research or business applications.
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About the Instructor
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Christopher Walters is an Associate Professor in the Department of Economics
 at the University of California, Berkeley.
 He joined the Berkeley faculty in 2013 after completing a PhD in economics
 at MIT under the supervision of Joshua Angrist and Parag Pathak.
 Professor Walters is an applied microeconomist with expertise in labor
 economics, applied econometrics, causal inference, and the economics of
 education.
 His recent work includes studies using experiments to detect labor market
 discrimination, evaluations of school choice and early childhood programs,
 and empirical Bayes methods for summarizing heterogeneity in treatment
 effects.
 His research has been published in peer-reviewed outlets including 
\emph on
The Quarterly Journal of Economics, The American Economic Review, The Journal
 of Political Economy, 
\emph default
and 
\emph on
Econometrica
\emph default
.
 Professor Walters has given several public lectures and workshops on empirical
 Bayes methods and is preparing a forthcoming Handbook of Labor Economics
 chapter on applications of these methods in labor economics.
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Course Schedule
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This session will be organized in three 3-hour lectures on Monday 11/6,
 Wednesday 11/8, and Friday 11/10, with each lecture from 6-9pm Eastern
 time.
 Coding labs and live-coding of solutions will be used to illustrate and
 apply the methods discussed in lectures.
 
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Approximate Schedule (all times Eastern):
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Monday 11/6
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6:00-7:00pm
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Lecture: Empirical Bayes framework and recipe, linear shrinkage, 
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James/Stein theorem
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7:00-7:10pm
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7:10-8:10pm
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Lecture: Combining estimators, EB and regression, EB decision rules, 
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connections to machine learning, EB vs.
 full Bayes
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8:10-8:20pm
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\emph on
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8:20-9:00pm
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Coding lab 1: School value-added
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Wednesday 11/8
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6:00-6:30pm
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Live-coding of solutions to lab 1
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6:30-6:40pm
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6:50-8:10pm
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Lecture: Flexible variance estimation, precision-dependence, deconvolution,
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non-parametric shrinkage
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8:10-8:20pm
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8:20-9:00pm
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Coding lab 2: Employer-level labor market discrimination
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'
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Friday 11/10
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6:00-6:50pm
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Live-coding of solutions to lab 2
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7:00-7:30pm
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Lecture: Large-scale inference – EB for multiple testing
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7:40-8:10pm
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Coding lab 3: Classifying employer-level discrimination
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8:10pm-8:40pm
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Live-coding of solutions to lab 3
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8:40pm-9:00pm
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Remaining questions and concluding remarks
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Reading List
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Angrist, Hull, Pathak, and Walters (2017).
 “Leveraging lotteries for school value-added: testing and estimation." 
\emph on
Quarterly Journal of Economics
\emph default
 132(2).
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Efron (2012).
 
\bar under
Large-scale inference: Empirical Bayes methods for estimation, testing,
 and prediction
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.
 Cambridge University Press.
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Kline and Walters (2021).
 "Experimental detection of job-level employment discrimination." 
\emph on
Econometrica
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 89(2).
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Kline, Rose, and Walters (2022).
 “Systemic discrimination among large US employers.” 
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Quarterly Journal of Economics
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 137(4).
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Kline, Rose and Walters 2023
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Koenker and Gu (2017).
 “REBayes: an R package for empirical Bayes mixture methods.” 
\emph on
Journal of Statistical Software
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 82(1).
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Morris (1983).
 
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Parametric Empirical Bayes Inference: Theory and Applications.
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\emph on
Journal of the American Statistical Association 
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78(381).
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Narasimhan, B., and Efron, B.
 (2020).
 
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deconvolveR: A g-modeling program for deconvolution and empirical Bayes
 estimation.
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\emph on
Journal of Statistical Software 
\emph default
94(11).
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