Randomization Inference When N = 1
- 2023-08-07 (Mon.), 10:30 AM
- Auditorium, B1F, Institute of Statistical Science；The tea reception will be held at 10:10.
- Online live streaming through Cisco Webex will be available.
- Dr. Tengyuan Liang
- Booth School of Business, University of Chicago
Abstract
Neyman’s seminal paper in 1923, which introduced the potential outcome framework and the analysis of randomized experiments, has arguably laid the foundation of causal inference for cross-sectional data. For time-series data, the framework of randomization inference is far less well-understood due to the interference: the potential outcomes at a particular time typically depend on treatments assigned before that time. Motivated by the literature of N-of-1 trials in clinical research and sequential AB testing in online marketing, in this talk, we study randomization experiments and causal inference when N = 1, borrowing insights from system identification and probability theory. The talk is based on joint work with Benjamin Recht (UC Berkeley).
Please click here for participating the talk online.
Please click here for participating the talk online.
Download
Update：2023-08-01 10:10
Back
