Regularization and confounding in linear regression for treatment effect estimation
type: Item
instance of: scholarly article
title: Regularization and confounding in linear regression for treatment effect estimation
DOI: 10.1214/16-BA1044
author: P. Richard Hahn
author: Jingyu He
author: Carlos M. Carvalho
author: David W. Puelz
published in: Bayesian Analysis
publication date: 2018-05-25T00:00:00Z
full work available at URL: https://arxiv.org/abs/1602.02176
full work available at URL: https://projecteuclid.org/euclid.ba/1484103680
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 6873722
zbMATH Keywords: causal inference
zbMATH Keywords: observational data
zbMATH Keywords: shrinkage estimation
describes a project that uses: BACprior
MaRDI profile type: Publication
cites work: Inference on Treatment Effects after Selection among High-Dimensional Controls
cites work: The horseshoe estimator for sparse signals
cites work: Treatment Effects: A Bayesian Perspective
cites work: Causal Inference for Statistics, Social, and Biomedical Sciences
cites work: Bayesian treatment effects models with variable selection for panel outcomes with an application to earnings effects of maternity leave
cites work: Q4164707
cites work: The effect of the prior distribution in the \textit{Bayesian Adjustment for Confounding} algorithm
cites work: Mixtures of g Priors for Bayesian Variable Selection
cites work: Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
cites work: The central role of the propensity score in observational studies for causal effects
cites work: Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models
cites work: Bayesian Effect Estimation Accounting for Adjustment Uncertainty
cites work: Confounder selection via penalized credible regions
cites work: Q3802438
description: scientific article; zbMATH DE number 6873722