Detecting Heterogeneous Treatment Effect with Instrumental Variables

no code implementations • 9 Aug 2019 • Michael Johnson, Jiongyi Cao, Hyunseung Kang

There is an increasing interest in estimating heterogeneity in causal effects in randomized and observational studies.

Interpretable Machine Learning

Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index

no code implementations • 30 Mar 2016 • Susan Athey, Raj Chetty, Guido Imbens, Hyunseung Kang

We focus primarily on a setting with two samples, an experimental sample containing data about the treatment indicator and the surrogates and an observational sample containing information about the surrogates and the primary outcome.

Causal Inference