Apologies to those who have already received this, but I thought this
workshop would be of interest to the Polmeth community. I would also be
grateful if you could recirculate within your department (faculty, graduate
students)
Best,
Josh
(On behalf of Bernard Black and Mathew McCubbins, the conference organizers)
Joshua Y. Lerner
Postdoctoral Research Fellow
Northwestern University Pritzker School of Law
*2018 Northwestern-Duke Main and Advanced Causal Inference Workshops *
------------------------------
*[please recirculate to others who might be interested]*
Northwestern University and Duke University are holding our “main”
week-long workshop on Research Design for Causal Inference – our ninth
annual workshop -- at Northwestern Law School in downtown Chicago. We
invite you to attend. Our apologies for the length of this message.
*Main Workshop: * Monday – Friday, June 18-22, 2018
We will also be holding an “Advanced” Workshop the following week:
*Advanced Workshop: *Monday – Wednesday, June 25-27, 2018
Both workshops will be taught by world-class causal inference researchers.
See below for details. Registration is limited to around 100
participants. In the past we have filled the main workshop quickly. So
please register soon.
For information and to register: www.law.northwestern
.edu/research-faculty/conferences/causalinference/
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.law.northwestern.edu_research-2Dfaculty_conferences_causalinference_&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=0ouFG0RgOEJC_Wn7KgHHrCPJ3WFEzSSspfd21g_LkcU&e=>
*Workshop Organizers*
*Bernard Black (Northwestern University)*
Bernie Black is Nicholas J. Chabraja Professor at Northwestern University,
with positions in the Pritzker School of Law, the Institute for Policy
Research, and the Kellogg School of Management, Finance Department.
Principal research interests: health law and policy; empirical legal
studies, law and finance, international corporate governance. Web page
with link to CV: www.law.northwestern.edu/faculty/profiles/BernardBlack/
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.law.northwestern.edu_faculty_profiles_BernardBlack_&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=W1QfYlXATnlo7t4apWkv-xQIfcI4O657IsF-cNK3CIs&e=>.
Papers on SSRN: http://ssrn.com/author=16042
<https://urldefense.proofpoint.com/v2/url?u=https-3A__papers.ssrn.com_sol3_cf-5Fdev_AbsByAuth.cfm-3Fper-5Fid-3D16042&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=SAiY30eBnT6oQy7qlBxwWbcgQU2T7D8K916_I_Fz9M8&e=>
.
*Mathew McCubbins (Duke University) *
Professor of Political Science and Law at Duke University, with positions
in the Political Science Department and the Law School, 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
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.mccubbins.us_&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=mgdAHKxlKulzBWoG0YfkEmo-xXwmgrTPJ-kvc0HP9sE&e=>.
Papers on SSRN: http://ssrn.com/author=17402
<https://urldefense.proofpoint.com/v2/url?u=https-3A__papers.ssrn.com_sol3_cf-5Fdev_AbsByAuth.cfm-3Fper-5Fid-3D17402&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=1l9_dg8uaBQvE_Xa5AyvD7JiEpLRsn2uoZ4EIHdRKBw&e=>
.
*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, 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 credible causal inference and
which methods to use with messy, real-world datasets and limited sample
sizes. Several workshop days will include a Stata “workshop” to illustrate
selected methods with real data and Stata code.
*Advanced Workshop Overview: * The advanced workshop provides in-depth
discussion of selected topics that are beyond what we can cover in the main
workshop. Principal topics for 2018 include: Day 1 (Mon.): Principal
stratification (generalization of causal-IV concepts and applications,
including sample censoring through death or attrition. Day 2 (Tues.):
Direct and indirect causal effects. Synthetic controls and other advanced
“matching” approaches with emphasis on panel data sets. Day 3 (Wed.):
Application of machine learning methods to causal inference.
*Target audience for 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.
*Target Audience for Advanced Workshop:* 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 potential outcomes notation, difference-in-differences,
regression discontinuity, panel data, and instrumental variable designs,
but will not assume expertise in any of these areas.
*Main 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:
https://statistics.fas.harvard.edu/people/donald-b-rubin
<https://urldefense.proofpoint.com/v2/url?u=https-3A__statistics.fas.harvard.edu_people_donald-2Db-2Drubin&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=mYL8SeHQWxd4cwx-rcPRzMgCYpFLpSeqdicox75hAIs&e=>;
Wikipedia: http://en.wikipedia.org/wiki/Donald_Rubin
<https://urldefense.proofpoint.com/v2/url?u=https-3A__en.wikipedia.org_wiki_Donald-5FRubin&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=OkqS_58Zg7JoNEw0HxbqiSH9Vflc-g1QpX6OZh56LOY&e=>
*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/
<https://urldefense.proofpoint.com/v2/url?u=https-3A__eml.berkeley.edu_-7Ejmccrary_&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=2OBRe8ysDQdOfU1qSxDT1EY_WYwvvI69kdDbg9ZP1jQ&e=>
.
*Jens Hainmueller (Stanford University, Department of Political Science)*
Jens Hainmueller is Professor in the Stanford Political Science Department,
and co-Director of the Stanford Immigration Policy Lab. 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//
<https://urldefense.proofpoint.com/v2/url?u=https-3A__web.stanford.edu_-7Ejhain_&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=4AWObO0mgcFmRD7RnW7g3WahwW-0x40pwP1bekUwAXg&e=>.
Papers on SSRN: https://ssrn.com/author=739013
<https://urldefense.proofpoint.com/v2/url?u=https-3A__papers.ssrn.com_sol3_cf-5Fdev_AbsByAuth.cfm-3Fper-5Fid-3D739013&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=ymqCw2da7LlYwAVArJUu8kVaWDgnDuXUom9VQRNMwYI&e=>
.
*Advanced Workshop Faculty (in order of appearance)*
*Donald Rubin (see above)*
*Fabrizia Mealli (University of Florence, Department of Statistics and
Computer Science)*
Fabrizia Mealli is Professor of Statistics at the University of Florence
and external research associate at the Institute for Social and Economic
Research (ISER) at the University of Essex. Her research focuses on causal
inference and simulation methods, program evaluation, missing data, and
Bayesian inference. She is a fellow of the American Statistical
Association, and associate editor of Journal of the American Statistical
Association (JASA), Biometrics, and Annals of Applied Statistics. Web page
with link to CV: http://local.disia.unifi.it/mealli/
<https://urldefense.proofpoint.com/v2/url?u=http-3A__local.disia.unifi.it_mealli_&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=VRQ2dQ-gAdNxK5bhqi8MCZOu1SRz4Rox9U3Zgp1A7Bc&e=>
*Yiqing Xu (University of California San Diego, Department of Political
Science)*
Yiqing Xu is Assistant Professor of Political Science at University of
California, San Diego. His main methods research involves causal inference
with panel data. Website: http://yiqingxu.org/
<https://urldefense.proofpoint.com/v2/url?u=http-3A__yiqingxu.org_&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=oGKtNn1Zpcm0PnXjMgGqfHqz3b3BJpVHEhma8de-xoM&e=>
.
*Justin Grimmer (University of Chicago, Department of Political Science)*
Justin Grimmer is Associate Professor of Political Science at the
University of Chicago. His primary research interests include political
representation, Congressional institutions, and text as data methods.
Website:https://www.justingrimmer.org/
<https://urldefense.proofpoint.com/v2/url?u=https-3A__www.justingrimmer.org_&d=DwMF-g&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=lUN81ZV0TOY2M613-9fp7fvCoYCAyCjHnUn7PakFuz8&m=2z9iHINqHe4h262ctfK6XHu5owt_UCB9M5REdr56X1I&s=udGBbYiK5vFFTClNv1MdVAaOSZyNRalBNRMG6WzqOsI&e=>
*Main Workshop Outline*
*Monday June 18 (Donald 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. Rerandomization. One-sided and two-sided
noncompliance.
*Tuesday June 19 (Justin McCrary): Matching and Reweighting Designs for
“Pure” Observational Studies*
The core, untestable requirement of selection [only] on observables.
Ensuring covariate balance and common support. Subclassification,
matching, reweighting, and regression estimators of average treatment
effects. Propensity score methods. Methods that aim directly at covariate
balance.
*Wednesday June 20 (Justin McCrary): 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 June 21 (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. Introduction to synthetic controls.
*Friday morning June 22 (Jens Hainmueller): 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. Additional
parallel sessions if needed to meet demand.
*Stata and R sessions*
On Tuesday, Wednesday, and Thursday, we will either run parallel Stata and
R sessions to illustrate actual code to implement the designs discussed in
the lectures, or build Stata code into the lecture slides.
*Advanced Workshop Outline*
*Monday June 25 (Donald Rubin and Fabrizia Mealli): Principal
Stratification and Censoring*
Generalizing the causal-IV strata of compliers-always takers-never
takers-defiers. Which treatment effects can be estimated for which
strata? Handling missing data and censoring through “death” or attrition.
*Tuesday June 26 morning (Donald Rubin and Fabrizia Mealli): Direct and
indirect causal effects. *
“Mediation” analysis: Direct and indirect causal effects versus principal
associative and dissociative effects.
*Tuesday June 26 afternoon (Yiqing Xu): Advanced matching*
Advanced matching and reweighting methods, with an emphasis on panel data
applications. Generalized synthetic controls. Relative strengths and
weaknesses of different matching and reweighting approaches.
*Wednesday June 27 (Justin Grimmer): Machine learning (predictive
inference) meets causal inference*
Introduction to machine learning approaches. When and how can machine
learning methods be applied to causal inference questions.
*Registration and Workshop Cost*
*Main Workshop: *tuition is $900 ($600 for graduate students (PhD, SJD, or
law) and post-docs. The workshop fee include all materials, temporary
Stata 15 license, breakfast, lunch, snacks, and an evening reception on the
first workshop day.
*Advanced Workshop: *tuition is $600 ($400 for graduate students (PhD,
SJD, or law) and post-docs. There is a $100 discount for persons attending
both workshops.
You can cancel from either workshop five weeks in advance (May 14 for main
workshop, May 21 for advanced workshop) for a 75% refund and by three weeks
in advance 50% refund (in each case, less credit card processing fee), but
there are no refunds after that.
We know the workshop is 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.
*Questions about the workshops*
Please email Bernie Black ([log in to unmask]) or Mat McCubbins (
[log in to unmask]) for substantive questions or fee waiver
requests, and Laura Dimitrijevic ([log in to unmask]) for
logistics and registration.
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