See below, from Bernie Black and Mat McCubbins.

Best,
ADM

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2013 Causal Inference Workshops:  Main and Advanced

We are holding two workshops on Research Design for Causal Inference this year, and invite you to attend either or both.  Apologies for the length of this message, which covers both.
Main workshop:  Monday – Friday, June 24-28, 2013
Advanced workshop:  Monday - Wednesday, August 12-14, 2013.
 
Both will be held at Northwestern Law School, Chicago, Illinois, and will be taught by world-class causal inference researchers.  See below for details.  Registration for each is limited to 100 participants.  We filled the main workshop quickly last year, so please register soon.
 
For information and to register:  law.northwestern.edu/faculty/conferences/causalinference/
Bernie Black [Northwestern, Law School and Kellogg School of Management]
Mat McCubbins [USC, Law School, Marshall School of Business, and Political Science Department]
 
Main Workshop Overview:  Research design for causal inference is at the heart of a “credibility revolution” in empirical research.  We will cover the design of true randomized experiments and contrast them to simulations and quasi-experiments, where part of the sample is “treated” in some way, and the remainder is a control group, but the researcher controls neither the assignment of cases to treatment and control groups nor administration of the treatment.  We will assess what causal inferences one can and cannot draw from a research design, threats to valid inference, and research designs that can mitigate those threats.
Most empirical methods courses survey a variety of methods.  We will begin instead with the goal of causal inference, and discuss how to design research to come closer to that goal.  The methods are often adapted to a particular study.  Some of the methods we will discuss are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to prefer for messy, real-world datasets with limited sample sizes.  Each day will include with a Stata “workshop” to illustrate selected methods with real data and Stata code.
 
Target audience:  Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc), medicine, sociology, education, psychology, etc. – indeed anywhere that causal inference is important.
We will assume knowledge, at the level of an upper-level college econometrics or similar course, of multivariate regression, including OLS, logit, and probit; basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables.  Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable but more limited training.  Even for recent PhD’s, there will be much that you don’t know, or don’t know as well as you should.
 
Main workshop faculty
Guido Imbens (Stanford University)
Guido Imbens is Professor of Economics at Stanford University, Graduate School of Business. Principal research interests: econometric theory, applied econometrics, labor economics. Coauthor with Donald Rubin of Causal Inference in Statistics and Social Sciences (draft 2012). Papers on SSRN:  http://ssrn.com/author-20310
Justin McCrary (University of California, Berkeley, Law School)
Justin McCrary is Professor of Law, University of California, Berkeley.  Principal research interests: crime and urban problems, law and economics, corporations, employment discrimination, and empirical legal studies.  Web page with link to CV:http://www.econ.berkeley.edu/~jmccrary/.

Alberto Abadie (Harvard University, Kennedy School of Government)
Alberto Abadie is Professor of Public Policy at the Kennedy School of Government at Harvard University.  Principal research interests: econometrics; program evaluation.  Web page with link to CV:  http://www.hks.harvard.edu/fs/aabadie/ .  Papers on SSRN: http://ssrn.com/author=198468.
 
Main workshop outline
Monday-Tuesday June 24-25 (Guido Imbens)
Introduction to Modern Methods for Causal Inference
Overview of causal inference and the Rubin “potential outcomes” causal model.  The “gold standard” of a randomized experiment.  Treatment and control groups, and the core role of the assignment (to treatment) mechanism.  Causal inference as a missing data problem, and imputation of missing potential outcomes.
Observational Studies:  Matching and Propensity Score Reweighting
The core, untestable “selection [only] on observables” assumption.  The need for “overlap” between treated and control units.  Propensity scores, inverse propensity score reweighting; matching methods.  Trimming poorly matched observations.  Average treatment effects on the treated (ATT), the controls (ATC) and the whole sample (ATE).  Near-equivalence of matching and reweighting.
Wednesday June 26 (Justin McCrary)
Instrumental variable and regression discontinuity methods
Instrumental variables (IV), including (i) the core (untestable) need to satisfy the “only through” exclusion restriction, (ii) heterogeneous treatment effects; (iii) randomized trials (or quasi-experiments) with noncompliance.   (Regression) discontinuity (RD) research designs: sharp and fuzzy designs; bandwidth choice; need to test (not just assume) covariate balance; discontinuities as substitutes for true randomization and as sources of convincing instruments.
Thursday-Friday, June 27-28 (Alberto Abadie)
Difference-in-Differences, Panel Data, and Synthetic Controls
Simple two-period DiD; the “parallel changes” assumption.  Accommodating covariates.  Triple differences.  Panel data methods.  Synthetic controls.
Standard Errors, Directed Acyclic Graphs, Event Studies
Standard errors:  ordinary, robust, and clustered standard errors.  The bootstrap.  Introduction to directed acyclic graphs.  Event studies.
Friday afternoon:  Feedback on your own research
Attendees will present their own research design questions from current work in breakout sessions and receive feedback on research design.  Session leaders:  Bernie Black, Mat McCubbins, Alberto Abadie.  Parallel sessions as needed to meet demand.
_________________________________________________________________________
 
Advanced Workshop Overview and Target Audience:  The advanced workshop seeks to provide an in-depth discussion of selected topics at the causal inference research frontier.  Our target audience is empirical researchers who are familiar with the basics of causal inference (from our main workshop or otherwise), and want to extend their knowledge.
 
Advanced Workshop Faculty
Donald B. Rubin (Harvard University, Department of Statistics)
Donald Rubin is John L. Loeb Professor of Statistics, Harvard University.  His work on the “Rubin Causal Model” is central to modern understanding of when one can and cannot infer causation from regression.  Principal research interests:  statistical methods for causal inference; Bayesian statistics; analysis of incomplete data.  Web page, with link to CV: www.stat.harvard.edu/faculty_page.php?page=rubin.html; Wikipedia: http://en.wikipedia.org/wiki/Donald_Rubin
Ben Hansen (University of Michigan, Department of Statistics)
Ben Hansen is Associate Professor of Statistics, University of Michigan, and Faculty Associate at the University of Michigan Institute for Social Research.  Principal research interests: causal inference in observational studies, design-based inference for randomized experiments, statistical computing; applications to health outcomes research, social epidemiology, political science and education evaluation.  CV [w link].  Web page: http://www.stat.lsa.umich.edu/faculty/hansen_ben.html
Justin McCrary (University of California, Berkeley, Law School) [see blurb above]
 
Advanced Workshop Outline
Monday August 12 (Don Rubin)
Choosing estimands (the science).  Implications of choice of estimand for choice of method.  Principal stratification.  Flexible matching methods.  Multiple imputation of missing potential outcomes.  And whatever else Don thinks he should cover, in the allotted time.
Tuesday August 13 (Ben Hansen)
Advanced matching. This session focuses on the use of propensity-score based, optimal matching methods to reliably secure covariate balance and, with additional assumptions, consistency and robustness of causal effect estimation. Methods are demonstrated with code and exercises in R. Propensity score matching is the best known of a number of techniques that play roles both in study design and in statistical analysis; the session includes a comparative discussion of alternative matching and weighting methods.
Wednesday August 14 (Justin McCrary)
Conducting simulation studies.  Generalized method of moments (GMM) as a tool for estimating treatment effects and standard errors, including adjusting standard errors for two-step estimation (e.g., reweighting).  Inference and testing using the bootstrap.  Topics in regression discontinuity design:  nonparametric estimation; Local linear regression and density estimation; choosing bandwidth and assessing sensitivity to bandwidth choice.
________________________________________________________________________
 
Registration and Workshop Cost
Main workshop tuition is $850 ($500 for graduate students (PhD, SJD, or law) and post-docs).  Advanced workshop tuition is $550 ($350 for graduate students and post-docs).  There are additional discounts (to $350 and $200) for Northwestern or USC-affiliated attendees.  The workshop fees include all materials, temporary Stata12 license, breakfast, lunch, snacks, and Monday evening reception.  All amounts will increase by $50 as we approach the workshop date (May 1 for the main workshop), but we may fill up before then.  See website for registration deadlines and cancellation policy.  We know the workshops are not cheap.  We use the funds to pay our speakers and for meals and other expenses; we don’t pay ourselves.
 
Workshop Organizers
Bernard Black (Northwestern University, Law and Kellogg School of Management)
Bernie Black is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Law School and Kellogg School of Management.  Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies.  Papers on SSRN:  http://ssrn.com/author=16042.
Mathew McCubbins (University of Southern California)
Mat McCubbins is Provost Professor of Business, Law and Political Economy at University of Southern California, with positions in the Marshall School of Business, the Gould School of Law, and the Department of Political Science.  Principal research interests: legislative organization;communication, learning and decisionmaking; research design; network economics.  Web page with link to CV:  http://weblaw.usc.edu/who/faculty/directory/contactInfo.cfm?detailID=1432.  Papers on SSRN:  http://ssrn.com/author=17402.
 
Questions about the workshops:  Please email Bernie Black ([log in to unmask]) or Mat McCubbins  ([log in to unmask]) for substantive questions or fee waiver requests, and Michael Cooper ([log in to unmask] for logistics and registration.
 

--
Andrew D. Martin, Ph.D.
Charles Nagel Chair of Constitutional Law and Political Science	
Vice Dean and CERL Director, School of Law	
Washington University in St. Louis

(314) 935-5863 (Office)
(314) 935-3836 (Fax)

Email: [log in to unmask]
WWW: http://adm.wustl.edu

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