Welcome to the “Special Guest Seminar Series“!

The Seminar will be held on-site and online Friday 7th October 2022 from 10.30 – 11.30 am. 

Our guest will be Kosuke Imai – Professor of Government and of Statistics, Harvard University.

The seminar will be held in Aula 205 (ex 32) – Viale Morgagni 59. The Seminar will be available also online. Please register here to participate online: https://us02web.zoom.us/webinar/register/WN_vqVkwNmmSp2194Ne3Z4WsQ

Title: Statistical Inference for Heterogeneous Treatment Effects and Individualized Treatment Rules Discovered by Generic Machine Learning in Randomized Experiments

Abstract: Researchers are increasingly turning to machine learning (ML) algorithms to estimate heterogeneous treatment effects (HET) and develop individualized treatment rules (ITR) using randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain HET or produce efficacious ITR under practical settings with many covariates and small sample size. In addition, the quantification of estimation uncertainty remains a challenge. We develop a general approach to statistical inference for estimating HET and evaluating ITR discovered by a generic ML algorithm. We utilize Neyman’s repeated sampling framework, which is solely based on the randomization of treatment assignment and random sampling of units.  Unlike some of the existing methods, the proposed methodology does not require modeling assumptions, asymptotic approximation, or resampling methods. We extend our analytical framework to a common setting, in which the same experimental data is used to both train ML algorithms and evaluate HET/ITR. In this case, our statistical inference incorporates the additional uncertainty due to random splits of data used for cross-fitting.