BibTex format

author = {Graham, DJ and McCoy, EJ and Stephens, DA},
doi = {10.1214/14-BA928},
journal = {Bayesian Analysis},
pages = {47--69},
title = {Approximate Bayesian Inference for Doubly Robust Estimation},
url = {},
volume = {11},
year = {2015}

RIS format (EndNote, RefMan)

AB - Doubly robust estimators are typically constructed by combining outcomeregression and propensity score models to satisfy moment restrictions thatensure consistent estimation of causal quantities provided at least one of the componentmodels is correctly specified. Standard Bayesian methods are difficult toapply because restricted moment models do not imply fully specified likelihoodfunctions. This paper proposes a Bayesian bootstrap approach to derive approximateposterior predictive distributions that are doubly robust for estimation ofcausal quantities. Simulations show that the approach performs well under varioussources of misspecification of the outcome regression or propensity score models.The estimator is applied in a case study of the effect of area deprivation on theincidence of child pedestrian casualties in British cities.
AU - Graham,DJ
AU - McCoy,EJ
AU - Stephens,DA
DO - 10.1214/14-BA928
EP - 69
PY - 2015///
SN - 1936-0975
SP - 47
TI - Approximate Bayesian Inference for Doubly Robust Estimation
T2 - Bayesian Analysis
UR -
UR -
VL - 11
ER -