Empirical evidence in social and biomedical sciences commonly suggests individual’s response to public policy or medical treatment is heterogeneous. How to efficiently learn and exploit such heterogeneity for the purpose of designing personalized policy/treatment are important topics of interdisciplinary interests.
This one-day workshop presents recent developments on evidence-based design of personalized treatment and targeting policies. It aims to bring together students and researchers in economics, epidemiology, medicine and statistics, with research interest in
– Econometric and machine learning methods for personalized treatment/policy
– Medical or policy decision under ambiguity
– Meta-analysis for medical or policy decision making
Confirmed speakers include
Jason Abaluck (Yale)
Karun Adusumilli (U Penn)
Rachel Cassidy (IFS)
Sukjin Han (UT Austin)
Charles Manski (Northwestern)
Stefan Wager (Stanford)
