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.
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.
Statistical software. We will use Stata, and will provide attendees with a temporary Stata12 license. Versions 10 and 11 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), 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.
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions.
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 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.
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.
Reading: Imbens and Rubin, Causal Inference in Statistics and Social Sciences, chapters 1-8 (chapter 2 is background and can be skipped).
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.
Reading: Imbens and Rubin, Causal Inference in Statistics and Social Sciences, chapters 12-21, 23
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.
Reading: Angrist and Pischke, Mostly Harmless Econometrics, chaps. 2, 4
(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.
Readings: Justin McCrary and Heather Royer (2011), The Effect of Female Education on Fertility and Infant Health: Evidence from School Entry Laws Using Exact Date of Birth", 101 American Economic Review 158–95.
David S. Lee (2008), Randomized Experiments from Non-random Selection in U.S. House Elections, 142 Journal of Econometrics 675-697.
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.
Readings: Abadie, Alberto, Alexis Diamond and Jens Hainmueller (2010), Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program, 105 Journal of the American Statistical Association 493-505.
Angrist, Joshua, and Jorn-Steffen Pischke (2009), Mostly Harmless Econometrics: An Empiricist’s Companion ch. 5.
Recommended (but we know it’s another book to buy): Cameron, A. Colin, and Pravin Trivedi (2005), Microeconometrics, Methods and Applications chs. 21-23
Thursday post-lunch talk (Bernie Black)
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.
Readings: Abadie, Alberto, and Javier Gardeazabal (2003), The Economic Costs of Conflict: A Case Study of the Basque Country, 93 American Economic Review 113-132.
Angrist, Joshua, and Jorn-Steffen Pischke (2009), Mostly Harmless Econometrics: An Empiricist’s Companion ch. 8.
Recommended: Cameron and Trivedi (2005), Microeconometrics, Methods and Applications ch. 11.
Morgan, Steven, and Christopher Winship (2007) Counterfactuals and Causal Inference: Methods and Principles for Social Research ch. 3.
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).
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:
A number of other hotels in the area also offer discounts to Northwestern guests, visit our hotel information page for additional options.