There are some heavy hitters presenting:
2022 Northwestern Main and Advanced Causal Inference Workshops
[please recirculate to others who might be interested]
After a COVID break during 2020 and 2021, we are excited to be holding our 11th annual workshop on Research Design for Causal Inference at Northwestern Law School in Chicago, IL. We invite you to attend. Our apologies for the length of this message.
Main Workshop: Monday – Friday, August 8-12, 2022
Advanced Workshop: Monday – Wednesday, August 15-17, 2022
What’s special about these workshops are the speakers. Both will be taught by world-class causal inference researchers. See below for details. Registration is limited to 125 participants for the main workshop, and 100 for the advanced workshop. In the past we have filled the main workshop quickly, and we expect there may be pent up demand after the 2-year COVID break, so please register soon.
For information and to register:
Main Workshop Overview: 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 which units are treated vs. control, nor administration of the treatment. We will assess the causal inferences one can draw from specific “causal” research designs, threats to valid causal inference, and research designs that can mitigate those threats.
Advanced Workshop Overview: The advanced workshop provides in-depth discussion of selected topics that are beyond what we can cover in the main workshop. The principaltopics for 2022 quantile and nonlinear difference-in-differences, doubly robust estimation of causal effects; DiD methods that address staggered treatments (applied to different units at different times); and the application of machine learning methods to causal inference.
Main Workshop Outline
Monday, August 8 (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, August 9 (Pedro Sant’Anna): Matching and Reweighting Designs
The core, untestable requirement of selection [only] on observables. Ensuring covariate balance and common support. Matching, reweighting, and regression estimators of average treatment effects. Propensity score methods.
Wednesday, August 10 (Pedro Sant’Anna): Panel Data and Difference-in-Differences
Panel data methods: pooled OLS, random effects, and fixed effects. Simple two-period DiD and panel data extensions. The core “parallel trends” assumption. Testing this assumption. Event study (leads and lags) and distributed lag models. Accommodating covariates. Triple differences. Robust and clustered standard errors.
Thursday, August 11 (Rocio Titiunik or Matias Cattaneo): Regression Discontinuity
Regression discontinuity (RD) designs: sharp and fuzzy designs; continuity-based methods and bandwidth selection; local randomization methods and window selection; empirical falsification of RD assumptions; extensions and generalizations of canonical RD setup: discrete running variable, multi-cutoff, multi-score, and geographic designs. RD software website: https://sites.google.com/site/rdpackages/
Friday, August 12: Morning (Matias Cattaneo or Rocio Titiunik): Instrumental variable methods
Causal inference with instrumental variables (IV): the role of the exclusion restriction and first stage assumption; the monotonicity assumption and local average treatment effect (LATE) interpretation; applications to randomized experiments with imperfect compliance, including intent-to-treat designs and two-stage estimation. Connections between IV and fuzzy RD designs.
Friday, August 12: 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, Scott Cunningham, Rocio Titiunik or Matias Cattaneo). Additional parallel sessions if needed to meet demand.
Advanced Workshop Outline
Monday, August 15: Jeffrey Wooldridge: Advanced matching and balancing methods
Choosing among the many available matching and balancing methods. Estimators that aim directly at covariate balance. Combining balancing with regression and doubly robust estimators in cross-sectional and panel data settings. Synthetic controls.
Tuesday, August 16: Yiqing Xu: Advanced panel data methods
Causal inference with panel data using parametric, semi-parametric, non-parametric methods for addressing imbalance between treated and control units. Bias in classic DiD models using two-way fixed effects.
Wednesday, August 17: Christian Hansen: Introduction to machine learning (predictive inference) and applications to causal inference
Introduction to “machine-learning” approaches to prediction algorithms. When and how can machine learning methods be applied to causal inference questions.
Registration and Workshop Cost
Main Workshop: tuition is $900 ($600 for post-docs and graduate students; $500 if you are Northwestern-affiliated). The workshop fee includes all materials, temporary Stata license, breakfast, lunch, snacks, and an evening reception on the first workshop day.
Advanced Workshop: tuition is $600 ($400 for post-docs and graduate students; $300 if you are Northwestern affiliated).
$200 discount for persons attending both workshops
Bernard Black (Northwestern University)
Scott Cunningham (Baylor University)
Questions about the workshops: Please email Bernie Black (email@example.com) or Scott Cunningham (firstname.lastname@example.org) for substantive questions or fee waiver requests, and Sarah Jane King Shoemaker (email@example.com) for logistics and registration.