UAI 2021poster43 citations
Invariant representation learning for treatment effect estimation
Claudia Shi, Victor Veitch, David M. Blei
Abstract
The defining challenge for causal inference from observational data is the presence of ‘confounders’, covariates that affect both treatment assignment and the outcome. To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding. However, including every observed covariate in the adjustment runs the risk of including ‘bad controls’, variables that
BibTeX
@InProceedings{pmlr-v161-shi21a,
title = {Invariant representation learning for treatment effect estimation},
author = {Shi, Claudia and Veitch, Victor and Blei, David M.},
booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
pages = {1546--1555},
year = {2021},
editor = {de Campos, Cassio and Maathuis, Marloes H.},
volume = {161},
series = {Proceedings of Machine Learning Research},
month = {27--30 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v161/shi21a/shi21a.pdf},
url = {https://proceedings.mlr.press/v161/shi21a.html},
abstract = {The defining challenge for causal inference from observational data is the presence of ‘confounders’, covariates that affect both treatment assignment and the outcome. To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding. However, including every observed covariate in the adjustment runs the risk of including ‘bad controls’, variables that