About this document
Average Treatment Effects in ML by Marc Romaní is a document available to read on EtoBox.
This document summarizes a lecture on average treatment effects from a machine learning and causal inference course. It introduces the potential outcomes framework for causal inference in randomized experiments. It describes how average treatment effects can be estimated by taking the difference between potential outcomes under treatment and no treatment. It provides an example where the potential outcome is daily air quality index and the treatment imposes restrictions on driving.
- Author
- Marc Romaní
- Language
- EN