Advanced Workshop - Monday, August 19 - Wednesday, August 21
We would like to invite you to attend the Fifth Annual Advanced Workshop on Research Design for Causal Inference, which builds on our "main" workshop. The workshop will be hosted by Duke Law and Northwestern in Room 154 of the Physics Building, 120 Science Drive, Durham NC. The workshop will run daily from 9:00am - 5:00pm. Breakfast will be served at 8:30 each morning and lunch will be provided.
For questions about the workshop logistics or registration, please email Isabel Fox (firstname.lastname@example.org). Please email Bernie Black (email@example.com) or Mat McCubbins (firstname.lastname@example.org) for substantive questions or fee waiver requests.
The Advanced Workshop has reached capacity for 2019. We have reserved a limited number of spaces for faculty and researchers who can still register. Graduate students who wish to be added to the wait list can email Isabel Fox at email@example.com.
Faculty | Registration | Schedule | Questions | Hotels | Directions
The advanced workship provies in-depth discussion of selected topics that are beyond what we can cover in the main workshop. The principal topics for 2019 include:
Monday and Tuesday morning: Advanced matching and balancing methods, including synthetic controls, methods that aim at exact coveriate balance, and balancing with panel data.
Tuesday afternoon and Wednesday: Application of machine learning methods to causal inference. Tuesday afternoon will be an introdution to methods; Wednesday will be for applications to causal inference; where machine learning approaches are and are not useful.
Empirical researches who are familiar with the basics of causal inference (from our main workshop or otherwise), and want to extend thier knowledge. We will assume familiarity, but not expertise, with potential outcomes, difference-in-differences, regression discontinuity, and panel data.
Jann Spiess is Post-Doctoral Researcher at Microsoft Research and Assistant Professor of Operations, Information & Technology at Stanford Graduate School of Business. He is coauthor, with Sendhil Mullainathan, of Machine Learning: An Applied Econometric Approach (Journal of Economic Perspectives, 2017). His research focuses on integrating insights and techniques from machine learning into econometrics.
Yiqing Xu (UC San Diego, Political Science Department; Stanford University, Political Science starting 2019)
Yiqing Xu is Assistant Professor of Political Science at University of California, San Diego. He will be joining the Political Science department at Stanford University in 2019. His main methods research involves causal inference with panel data.
The Advanced Workshop has reached capacity for 2019. We have reserved a limited number of spaces for faculty and researchers who can still register here. Please email Isabel Fox at firstname.lastname@example.org if you have any questions or would like to be added to the wait list.
Advanced workshop tuition is $600; $400 for graduate students (PhD, SJD, or law) and post-docs; $300 for attendees affiliated with Duke or Northwestern who register with a valid duke.edu or northwestern.edu email. This includes breakfast and lunch each day. There is a $200 discount for people attending both workshops who are not affliated with Duke or Northwestern.
You can cancel by July 8, 2019 for a 75% refund and by July 29, 2019 for a 50% refund. There will be no refunds after this date.
Monday, August 19 and Tuesday, August 20: Morning (Yiqing Xu)
Advanced Panel Data Methods
Advanced topics for causal inference with panel data using parametric, semi-parametric, non-parametric methods for addressing imbalance between treated and control units. Topics include interactive fixed effects and matrix completion methods, as well as reweighting approaches such as panel matching, trajectory balancing and augmented synthetic control. Relative strengths and weaknesses of different methods will be discussed.
Stata code for causal inference with panel data: code and examples from Vladimir Atanasov and Bernard Black, The Trouble with Instruments: The Need for Pre-Treatment Balance in Shock-IV Designs (working paper 2018)
Tuesday, August 20: Afternoon (Jann Spiess)
Introduction to Machine Learning (Predictive Inference)
Introduction to “machine-learning” approaches to prediction algorithms. High-dimensional model selection (function classes, regularization, tuning), model combination (ensemble models, bagging, boosting), model evaluation, and implementation.
Wednesday, August 21 (Jann Spiess)
Applications of Machine Learning to Causal Inference
When and how can machine learning methods be applied to causal inference questions. Limitations (prediction vs estimation) and opportunities (data pre-processing, prediction as quantity of interest, high-dimensional nuisance parameters), with examples from an emerging empirical literature.
Please email Bernie Black (email@example.com) or Mat McCubbins (firstname.lastname@example.org) for substantive questions or fee waiver requests, and Isabel Fox (email@example.com) for logistics and registration.