2013 Main Causal Inference Workshop
We would like to invite you to attend the fourth annual workshop on Research Design for Causal Inference, sponsored by Northwestern University, the University of Southern California, and the Society for Empirical Legal Studies.
Monday-Friday, June 24-28, 2013, at Northwestern Law School, Chicago, IL
An Advanced Workshop on Research Design for Causal Inference will be held this year on August 12-14, 2013.
Teaching Faculty and Organizers | Registration | Workshop Schedule | Workshop Readings (login required) | Hotels
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 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 begin with the methods. They survey how each method works, and what assumptions each relies on. We will begin instead with the goal of causal inference, and discuss how to design research to come closer to that goal. The methods reflect the goal and are often adapted to the needs of 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 conclude with a Stata "workshop" where we will 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), 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 applied statistics course, of how to run multivariate regressions, including OLS, logit, and probit; familiarity with 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. 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.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions.
- Guido Imbens (Stanford University)
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 - Justin McCrary (University of California, Berkeley)
Professor of Law, University of California, Berkeley. Principal research interests: crime and urban problems, law and economics, corporations, employment discrimination, and empirical legal studies. Papers on SSRN - Alberto Abadie (Harvard University)
Professor of Public Policy at the Kennedy School of Government at Harvard University. Principal research interests: econometrics; program evaluation. Papers on SSRN
Conference Organizers
- Bernard Black (Northwestern University, Law and Kellogg School of Management)
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 - Mathew McCubbins (University of Southern California)
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. Papers on SSRN
Registration and Workshop Cost
Registration is limited to 100 participants. We filled up quickly last year, so please register soon.
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 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.
For Northwestern or USC-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.
Registration deadline: June 10, 2013.
You can cancel by May 15, 2013 for a 75% refund and by May 31, 2013 for a 50% refund (in each case, less credit card processing fee), but there are no refunds after that.
Questions about the workshop
Please email Bernie Black bblack@northwestern.edu or Mat McCubbins mmccubbins@law.usc.edu for substantive questions or fee waiver requests, and Michael Cooper causalinference@law.northwestern.edu for logistics and registration.
Workshop Schedule
Monday, June 24 (Guido Imbens)
Introduction to Modern Methods for Causal Inference; Analysis of Randomized Experiments
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.
Tuesday, June 25 (Guido Imbens)
Pure Observational Studies: Matching and Propensity Score Methods
The core, untestable "unconfoundedness" or "selection on observables" assumption. The need for overlap between treated and control units. Propensity scores, matching methods, blocking on the propensity score. Trimming to deal with lack of overlap. 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, June 27 (Alberto Abadie)
Difference-in-Differences (DiD), Panel Data, and Synthetic Controls
Difference-in-Differences: Simple two-period DiD; the “parallel changes” assumption. Accommodating covariates. Triple differences. Panel data methods. Synthetic controls.
Thursday post-lunch talk (Bernie Black)
Bloopers
Examples drawn from different areas of how to get causal inference wrong.
Friday morning, June 28 (Alberto Abadie)
Further Topics: 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 have an opportunity to present their own research design questions from current work in breakout sessions. Goal: obtain feedback on research design; not present results from a complete paper. Session leaders: Bernie Black, Mat McCubbins, Alberto Abadie. Parallel sessions as needed to meet demand) (15 min to present, 15 min discussion).
Hotels
June in Chicago is prime convention time, so hotel space is scarce and not cheap. We have arranged for a block of rooms at the Best Western in Evanston, plus morning and evening buses from the Best Western to the workshop and back:
Best Western Evanston, $79 for first 20 rooms, then $99 for next 30. Regular rate is $169. Conference rate expires May 7. Conference name: Causal Inference Workshop
A respectable, low-cost option if you prefer to stay in Chicago is the Howard Johnson Inn, 720 North Lasalle St, (312) 664-8100, $119 for their Stay and Save Program (pay in advance, 2-night minimum, non-cancelable) (prices may change)
Other options include:
- Allerton Hotel, 701 North Michigan Avenue Chicago IL, (312) 440-1500. $259/night plus tax (queen bed). Email Jerome Gray and ask for Northwestern rate. Older but nice hotel, smaller rooms. (prices may change)
- MileNorth Hotel, 166 East Superior Street, Chicago IL, (312) 787-6000. Closest to Northwestern, a bit nicer than the Allerton. Email: Jennifer Welch. $319/night plus tax for a standard room. (prices may change)
You might check online services, such as www.cheaphotels.com and www.hotwire.com. Hotwire can have especially good deals, but you won’t know what hotel you are at until the last minute.
A number of other hotels in the area also offer discounts to Northwestern guests, visit our hotel information page for additional options.
