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Instrumental Variables Methods: A Two-Day Workshop
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Professor:
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Peter Hull (Brown University)
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Website:
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Email:
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Twitter:
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Course Description
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Instrumental variables (IV) is a powerful tool for leveraging external (
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exogenous
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) variation to estimate the causal effects of otherwise confounded (“endogenous”
) variables.
 This two-day workshop will introduce the basics of IV through different
 practical examples, formalize the requirements of a valid and powerful
 IV, and discuss the mechanics of the two-stage least squares (2SLS) estimator.
 Special focus will be paid on interpreting linear IV under heterogeneous
 treatment effects and recent advances in leniency IV designs The course
 will include substantial group programming exercises, where different IV
 techniques will be illustrated in real-world applications.
 
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About the Instructor
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Peter Hull is the Groos Family Assistant Professor of Economics at Brown
 Univeristy and a Faculty Research Fellow at the National Bureau of Economic
 Research.
 He has published papers on topics in applied econometrics, education, healthcar
e, and criminal justice, in outlets such as the 
\emph on
American Economic Review
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, the 
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Quarterly Journal of Economics
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, the 
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Review of Economic Studies
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, and the 
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New England Journal of Medicine
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.
 His research is focused on developing and applying new instrumental variable
 methods to measure the quality of institutions, such as schools or hospitals,
 as well as discrimination and bias in human and algorithmic decision-making.
 Prior to Brown, Professor Hull taught at the Kenneth C.
 Griffin Department of Economics at the University of Chicago and worked
 at Microsoft Research and the Federal Reserve Bank of New York.
 He earned his PhD in economics from MIT in 2017, under 2021 Nobel Laureate
 Josh Angrist.
 
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Course Objectives
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This course is appropriate for students familiar with core causal inference
 tools (e.g.
 potential outcomes and/or causal graphs) and the basics of linear regression.
 The course will cover core topics on IV and 2SLS as well as more recent
 methodological advances; we will focus on applications and practical issues
 in the two coding labs.
 Students should be familiar with either Stata, R, or Python to complete
 these labs.
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Course Structure
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This is a two-day (6 hour) intensive workshop, with four hours of lectures
 and two 40-minute coding labs.
 The remaining time will be given to breaks.
 The coding labs will involve 20 minutes of self- or group-paced work and
 20 minutes of solution live-coding.
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Schedule
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Tuesday 9/27
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6:00-7:00pm
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Lecture 1: Regression Review; Regression Endogeneity; Introduction to IV
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7:00-7:10pm
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7:10-8:10pm
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Lecture 2: Understanding Instrument Validity; 2SLS Mechanics; Applications
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8:10-8:20pm
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8:20-9:00pm
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Coding Lab 1: Angrist and Krueger (1991)
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Wednesday 9/28
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6:00-7:00pm
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Lecture 3: Heterogeneous Treatment Effects; Characterizing Compliers; MTEs
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7:00-7:10pm
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7:10-8:10pm
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Lecture 4: Judge Leniency Designs; Shift-Share IV; New IV Frontiers
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8:10-8:20pm
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8:20-9:00pm
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Coding Lab 2: Stevenson (2018)
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9:00-9:15pm
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Closing Remarks
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