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• W1775588384 abstract "The objective of many studies in health and social sciences is to evaluate the causal effect of a treatment or exposure on a specific outcome using observational data. In such studies, the exposure is typically not randomized and therefore confounding bias can rarely be ruled out with certainty. The instrumental variable (IV) design plays the role of a quasi-experimental handle since the IV is associated with the treatment and only affects the outcome through the treatment. A valid IV can be used to obtain a test of the null hypothesis of no treatment effect with nominal type 1 error rate. Beyond testing for the causal null, one may wish to obtain an accurate estimate of the treatment causal effect. In this paper, we present a novel framework for identification and estimation using an IV of the marginal average causal effect of treatment amongst the treated (ETT) in the presence of unmeasured confounding. For inference, we propose three different semiparametric strategies: (i) inverse probability weighting (IPW), (ii) outcome regression, and (iii) doubly robust (DR) estimation which is consistent if either (i) or (ii) is consistent, but not necessarily both. An extensive simulation study is carried out to investigate the finite sample performance of the proposed estimators. The methods are further illustrated in a well known application of the impact of participation in a 401(k) retirement programs on savings." @default.
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• W1775588384 date "2015-06-26" @default.
• W1775588384 modified "2023-09-27" @default.
• W1775588384 title "Doubly Robust Estimation of a Marginal Average Effect of Treatment on the Treated With an Instrumental Variable" @default.
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• W1775588384 hasPublicationYear "2015" @default.
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