Purdue University’s Department of Statistics in the College of Science and the Krannert School of Management are co-organizing the 2021 Purdue Krannert-Statistics Machine Learning and Causal Inference Boot Camp. The boot camp will be held between July 13-15 and will be a hybrid event, including virtual as well as in-person speakers and participants.
The tutorials are aimed at Purdue graduate students and faculty interested in learning more about the principles and modern developments of causal inference. The boot camp will host distinguished invited speakers who will introduce participants to their own state-of-the-art contributions to causal machine learning. In addition, Purdue faculty members will provide demonstrations of the fundamental theories, tools, applications, and software for machine learning and causal inference during the boot camp.
Registration for the boot camp is now closed. Due to space- and COVID-restrictions, in-person participation will be limited to 30 registrations for each day. Once we receive requests for in-person participation, we will confirm in a follow up email.
The in-person sessions will be held on campus at RAWL 2082. The details for the virtual participation will be provided to the registrants.
Audience: Faculty and Graduate Students
Goals: Provide participants with a conceptual and practical introduction to aspects of modern causal inference with machine learning.
Organizers:
Mohammad S. Rahman
Vinayak Rao
Arman Sabbaghi
RAWL 2082 or Virtual
9:45 am: Welcome and Logistics
10:00 am: Introduction to fundamental concepts in Causal Inference and ML approaches of Causal Inference (Arman Sabbaghi, Purdue University)
12:00 pm: Lunch (served for the physical participants)
1:00 pm: Heterogeneous Treatment Effects (Stefan Wager, Stanford University)RAWL 2082 or Virtual
9:45 am: Causal Inference with Airbnb Data (Mohammad S. Rahman, Purdue University)
11:30 am: Keynote (Jasjeet Sekhon, Yale University)
12:30 pm: Lunch (served for the physical participants)
1:30 pm: Hands-on use of various libraries related to ML and Causal Inference. (Arman Sabbaghi, Purdue University + Yumin Zhang, Purdue University)RAWL 2082 or Virtual
12:00 pm: Introduction to fundamentals of Bayesian causal inference and related ML approaches (Richard Hahn, Arizona State University)
2:00-2:30 pm: Break
2:30 pm: Hands-on with Bayesian/ML aspects of Causal Inference (Vinayak Rao, Purdue University)Data and related materials (Purdue authentication required to access data)
Imbens and Rubin (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.
Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, Robins (2018). Double/debiased machine learning for treatment and structural parameters, The Econometrics Journal.
Athey, Tibshirani, Wager (2019). Generalized random forests. Annals of Statistics.
Künzel, Sekhon, Bickel, Yu (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences.
Hahn, Murray, Carvalho (2020). Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (with discussion). Bayesian Analysis.
Athey and Wager (2021). Policy Learning With Observational Data. Econometrica.
Howard, Ramdas, McAuliffe, Sekhon (2021). Time-uniform, nonparametric, nonasymptotic confidence sequences. The Annals of Statistics.
Rubin (1973a). Matching to remove bias in observational studies. Biometrics.
Rubin (1973b). The use of matched sampling and regression adjustments to remove bias in observational studies. Biometrics.
Rubin (1974). Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology.
Rubin (1978). Bayesian inference for causal effects: The role of randomization. Annals of Statistics.
Rosenbaum and Rubin (1983). The central role of the propensity score in observational studies for causal effects. Biometrika.
Rosenbaum and Rubin (1984). Reducing the bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association.
Rubin (1990). Comment on Neyman (1923) and causal inference in experiments and observational studies. Statistical Science.
Rosenbaum (2002). Observational Studies. Springer Verlag.
Rubin (2005). Causal inference using potential outcomes: Design, Modeling, Decisions. Journal of the American Statistical Association.
Rubin (2006). Matched Sampling for Causal Effects. Cambridge University Press.
Rosenbaum (2009). Design of Observational Studies. Springer Verlag.
