We would like to invite you to attend the fifth annual workshop on Research Design for Causal Inference, sponsored by Northwestern University, Duke University, and the Society for Empirical Legal Studies.
Monday-Friday, July 13-17, 2015, at Northwestern Law School, 375 East Chicago Avenue, Chicago, IL
Registration is limited to 100 participants. We filled up quickly last year, so please register soon.
An Advanced Workshop on Research Design for Causal Inference will be held this year on July 19-22, 2015, at Northwestern Law School.
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 the kinds of 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 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.
We will use Stata, and will provide attendees with a temporary Stata13 license. Versions 10, 11, and 12 should be fine for most purposes (but you may need to “downconvert” datasets. Each day will conclude with a Stata “workshop” where we will illustrate selected methods with real data and Stata code. More advice for Stata novices (pdf).
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.
Minimum prior knowledge
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.
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. For recent PhD's, there will be overlap with what you already know, but much that you don't know, or don't know as well as you should.
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions. The faculty are listed in order of appearance
Registration and Workshop Cost
Breakfast available at 8:30. “Lecture” sessions will run roughly 9:00-noon, lunch noon-1:00, sessions 1:00-3:00. “Stata sessions”: 3:15-4:15. All times except starting times are approximate. Please plan to arrive Sunday evening. Variations for particular days are indicated below. Return travel: The Friday afternoon sessions will end by 5:00, a return flight leaving 7:00 or later from O’Hare is safe; a bit earlier from Midway.
Monday, July 13 (Don Rubin)
Registration and breakfast: 8:00
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.
4:30-6:00: In central courtyard if weather permits.
Tuesday-Wednesday, July 14-15 (Jens Hainmueller)
Simple, block, pair, and cluster randomized trial designs. Different estimands. Fisher’s exact test. Covariate balance tests. One- and two-sided noncompliance. Issues in trial design. Threats to (internal) validity.
Difference-in-Differences and Panel Data
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.
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, July 16 (Stephen Morgan)
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.
Friday, July 17 (Justin McCrary)
Regression discontinuity methods
(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, Justin McCrary. Parallel sessions as needed to meet demand.
Questions about the workshops: Please email Bernie Black (firstname.lastname@example.org) or Mat McCubbins (email@example.com) for substantive questions or fee waiver requests, and Michael Cooper (firstname.lastname@example.org) for logistics and registration.