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This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the naı̈ve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate...
Persistent link: https://www.econbiz.de/10011434078
This paper studies inference for the average treatment effect in randomized controlled trials with covariate-adaptive randomization. Here, by covariate-adaptive randomization, we mean randomization schemes that first stratify according to baseline covariates and then assign treatment status so...
Persistent link: https://www.econbiz.de/10011309722
This paper studies inference for the average treatment effect in randomized controlled trials with covariate-adaptive randomization. Here, by covariate-adaptive randomization, we mean randomization schemes that first stratify according to baseline covariates and then assign treatment status so...
Persistent link: https://www.econbiz.de/10011459651
This paper studies inference for the average treatment effect in randomized controlled trials with covariate-adaptive randomization. Here, by covariate-adaptive randomization, we mean randomization schemes that first stratify according to baseline covariates and then assign treatment status so...
Persistent link: https://www.econbiz.de/10011648910
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Respondent-driven sampling is a commonly used method for sampling from hard-to-reach human populations connected by an …. This article reviews inferential methods available for data collected by respondent-driven sampling …
Persistent link: https://www.econbiz.de/10014114887