The Causal Inference program is a semester-long program to be presented in the spring of 2020. There will be significant themes regarding medical and health applications. Much of the new work in causal inference entails modern machine learning tools, and this perspective will be important to the program.
Program Working Groups
- Working Group I: Machine Learning Methods for Causal Inference, (Leader: Jason Poulos)
- Working Group II: Causal Discovery, (Leader: IIya Shpitser)
- Working Group III: Interference, (Leader: Michael Hudgens)
- Working Group IV: Computational Social Science, (Leaders: Alex Volfovsky and Tierney Graham)
- Working Group V: Causal Inference Methods for Pragmatic Trials, (Leader: Fan Li)
- Working Group VI: Dynamic Treatment Regime, (Leader: Qingyuan Zhao)
- Working Group VII: Missing Data, (Leader: Shu Yang)
- Working Group VIII: Bayesian Causal Inference, (Leaders: Fan Li)
- Working Group IX: Instrumental Variables and Natural Experiments, (Leaders: Luke Keele and Jason Poulos)
- Working Group X: Bridging Theory and Practice for Causal Inference, (Leader: Amy Nail)
Questions: email [email protected]
To see more information on research and other opportunities, visit the links below:
Visiting Research Fellows
Post-Doctoral Fellows
Participation in Workshops