2015 Northwestern-Duke Causal Inference Workshops

From my email:

Northwestern University and Duke University are holding two workshops on Research Design for Causal Inference this year.  They will run back-to-back at Northwestern Law School in downtown Chicago.  We invite you to attend either or both.  Apologies for the length of this message.

Main workshop:  Monday – Friday, July 13-17, 2015

Advanced workshop:  Sunday-Wednesday, July 19-22, 2015

Both workshops will be taught by world-class causal inference researchers.  See below for details.  Registration for each is limited to 100 participants.  In the past we have filled the main workshop quickly, so please register soon.

For information and to register:

http://www.law.northwestern.edu/research-faculty/conferences/causalinference/

Bernie Black [Northwestern, Law School and Kellogg School of Management]

Mat McCubbins [Duke, Political Science and Law]

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 “natural” or “quasi” experiments and to “pure observational studies,” 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 the causal inferences one can 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 emphasize how to design research to come closer to that goal.  The methods are often adapted to a particular study.  Some of the methods are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to use with messy, real-world datasets and limited sample sizes.  Each day will include with a Stata “workshop” to illustrate selected methods with real data and Stata code.

Advanced Workshop Overview:  The advanced workshop seeks to provide an in-depth discussion of selected topics that are beyond what we can cover in the main workshop.  Principal topics for 2015 include:  Day 1 (Sun.):  Simulation and bootstrapping (for standard errors, confidence intervals, and p-values).  Different bootstrap flavors and asymptotic refinement.  Day 1 can be skipped without affecting the rest of the workshop.  Day 2 (Mon.):  Selected issues for non-linear models, including logit, probit, and count models.  Using non-linear models with panel data.  Day 3 (Tues.):  Advanced matching methods.  Causal IV with covariates.  Day 4 (Wed.):  Causal mediation analysis.

Target audiences

Main Workshop:  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. –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.

Advanced Workshop.  Our target audience is empirical researchers who are reasonably familiar with the basics of causal inference (from our main workshop or otherwise), and want to extend their knowledge.  We will assume familiarity with the potential outcomes notation, randomization inference, difference-in-differences, regression discontinuity, panel data, and instrumental variable designs, but will not assume expertise in any of these areas.

Workshop faculty (in order of appearance)

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

Stephen L. Morgan (Johns Hopkins, Sociology and Education)

Stephen Morgan is Bloomberg Distinguished Professor of Sociology and Education, Johns Hopkins University.  He is a co-author (with Christopher Winship of Counterfactuals and Causal Inference:  Methods and Principles for Social Research (2nd ed. 2015).  Principal research interests:  inequality, education, and demography.  Web page with link to CV:  http://soc.jhu.edu/directory/stephen-l-morgan/

Jens Hainmueller (Stanford, Political Science)

Jens Hainmueller is Associate Professor in the Stanford Political Science Department.  He also holds a courtesy appointment in the Stanford Graduate School of Business.  His research interests include statistical methods, political economy, and political behavior.  Web page with link to CV:  http://www.stanford.edu/~jhain//

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 evaluationWeb page with link to CV:  http://www.hks.harvard.edu/fs/aabadie/ .  Papers on SSRN:  http://ssrn.com/author=198468.

Tyler VanderWeele (Harvard University, School of Public Health)

Tyler VanderWeele is Professor of Epidemiology and Professor of Biostatistics at the Harvard School of Public Health, and the author of Explanation in Causal Inference: Methods for Mediation and Interaction (Oxford University Press 2015).  Principal research interests: causal inference; mediation, interaction and spillover; epidemiology; religion and health.  Web page with link to CV:  http://www.hsph.harvard.edu/tyler-vanderweele/

Main workshop outline

Monday July 13 (Don Rubin)

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.  Choosing estimands (the science), and how the estimand affects research design.

Tuesday-Wednesday July 14-15 (Stephen Morgan)

Designs for “Pure” Observational Studies

Selection [only] on observables and common support assumptions.  Subclassification, matching, reweighting, and regression estimators of average treatment effects.  Propensity score methods.  What to match on: an introduction to directed acyclic graphs.

Instrumental variable methods

Causal inference with instrumental variables (IV), including (i) the core, untestable need to satisfy the “only through” exclusion restriction; (ii) heterogeneous treatment effects; and (iii) intent-to-treat designs for randomized trials (or quasi-experiments) with noncompliance.

Thursday-Friday July 16-17 (Jens Hainmueller)

Panel Data and Difference-in-Differences

Panel data methods:  pooled OLS, random effects, correlated random effects, and fixed effects.  Simple two-period DiD.  The core “parallel changes” assumption.  Testing this assumption.  Leads and lags and distributed lag models.  When does a design with unit fixed effects become DiD?  Accommodating covariates.  Triple differences.  Robust and clustered standard errors.

Regression Discontinuity

(Regression) discontinuity (RD) research designs: sharp and fuzzy designs; bandwidth choice; testing for covariate balance and manipulation of the threshold; discontinuities as substitutes for true randomization and sources of convincing instruments.

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, Jens Hainmueller.  Parallel sessions as needed to meet demand.

Advanced Workshop Outline

Sunday-Monday July 19-20 (Justin McCrary

Sunday:  Simulation and bootstrap

Conducting simulation studies.  Inference and testing using the bootstrap, including adapting bootstrap methods to your research design.  Different bootstrap flavors and asymptotic refinement.

Monday:  Non-linear methods

Selected issues for non-linear models, including logit, conditional logit, probit, and count models.  Using non-linear models with panel data.  Maximum likelihood and quasi maximum likelihood estimation.  Inconsistency of non-linear models with fixed effects.

Tuesday July 21 (Alberto Abadie)

Advanced Matching, DiD, and Causal IV

Selected topics in matching on covariates.  Synthetic controls. “Causal IV” with covariates and Abadie’s “magic kappa.”  Introduction to principal stratification, as a generalization of the complier-always taker-never taker-defier categories for causal IV

Wednesday July 22 (Tyler VanderWeele)

Causal mediation analysis — the direct and indirect effects of causes.  Comparison of traditional social science approaches to potential outcomes methods.  Identification.  Regression-based methods.  Sensitivity analysis. Multiple mediators.

________________________________________________________________________

Registration and Workshop Cost

Main workshop: tuition is $850 ($500 for graduate students (PhD, SJD, or law) and post-docs; $350 for Northwestern or Duke-affiliated attendees).

Advanced workshop: tuition is $700 ($400 for graduate students and post-docs; $250 for Northwestern or Duke-affiliated attendees).

3-day option (Mon.-Wed.)  The Sunday session (on simulation and bootstrapping) can be skipped without loss of continuity.  The 3-day cost is $550 ($300 for graduate students and post-docx; $200 for Northwestern or Duke affiliates).

Both workshops together:  40% discount on the advanced workshop for those who attend both workshops.  (Does not apply to Northwestern or Duke affiliates).

The workshop fees include all materials, temporary Stata13 license, breakfast, lunch, snacks, and an evening reception on the first day of each workshop.

Amounts will increase by $50 per workshop on May 15, 2015 (but the main workshop is likely to 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 Schedule

You should plan on full days, roughly 9:00-5:00.  Breakfast will be available at 8:30.

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 (Duke University)

Professor of Political Science and Law at Duke University, with positions in the Law School and the Political Science Department, and director of the Center for Law and Democracy.  Principal research interests: democratic institutions, legislative organization; behavioral experiments, communication, learning and decisionmaking; statutory interpretation, administrative procedure, research design; network economics.  Web page with link to CV:  www.mccubbins.us.  Papers on SSRN:  http://ssrn.com/author=17402.

Questions about the workshops:  Please email Bernie Black (bblack@northwestern.edu) or Mat McCubbins (mathew.mccubbins@duke.edu) for substantive questions or fee waiver requests, and Michael Cooper (causalinference@law.northwestern.edu for logistics and registration.

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