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 7-11, 2014, at Northwestern Law School, 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 August 13-15, 2014 at Duke University.
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. Click here for more advice for Stata novices.
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.
Registration and Workshop Cost
Click here to register.
Registration deadline: June 23, 2014.
You can cancel by May 23, 2014 for a 100% refund, June 13, 2014, for a 75% refund and by June 27, 2014 for a 50% refund (in each case, less credit card processing fee), but there are no refunds after that.
Tuition is $850; with a discounted rate of $500 for graduate students (PhD, SJD, or law) and post-doctoral fellows. The workshop fee includes all materials, a temporary Stata13 license, breakfast, lunch, snacks, and Monday evening reception. All amounts will increase by $50 as we approach the workshop date (May 22 for the main workshop), but we may fill up before then.
For Northwestern or Duke-affiliated attendees, we will charge the regular rate, but will give you a refund after the workshop to bring your cost down to $300 if you in fact attend at least a majority of the sessions. We adopted this policy because if you register and don’t come, you took a spot we could have provided to someone else.
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.
Monday-Tuesday, July 7-8 (Justin McCrary)
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.
Instrumental variable and regression discontinuity 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.
(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.
Wednesday, July 9 - Thursday morning July 10 (Alberto Abadie)
Observational Studies: Selection on observables
Selection on observables and common support assumptions. Subclassification, matching, and regression estimators of average treatment effects. Propensity score methods: matching and weighting. What to match on: a brief introduction to directed acyclic graphs.
Robust and clustered standard errors. The bootstrap.
Thursday afternoon, July 10 - Friday morning, July 11 (Jens Hainmueller)
Difference-in-Differences, Panel Data, and Synthetic Controls
Simple two-period DiD; the “parallel changes” assumption. Leads and lags and distributed lag models. Accommodating covariates. Triple differences. Panel data methods. Synthetic controls.
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.
Residence Inn Chicago Downtown/Magnificent
Click here to reserve your room
A respectable, low-cost option if you prefer to stay in Chicago is the Howard Johnson Inn, 720 North Lasalle St, (312) 664-8100, $99 for their Stay and Save Program (pay in advance, 2-night minimum, non-cancelable) (prices may change).
More hotel options to come
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.