Results 11 to 20 of about 2,029,988 (242)
Mhbounds - Sensitivity Analysis for Average Treatment Effects [PDF]
SSRN Electronic Journal, 2007 Matching has become a popular approach to estimate average treatment effects. It is based on the conditional independence or unconfoundedness assumption. Checking the sensitivity of the estimated results with respect to deviations from this identifying assumption has become an increasingly important topic in the applied evaluation literature.
Sascha O. Becker, Marco Caliendo
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Latent class instrumental variables and the monotonicity assumption
Emerging Themes in Epidemiology, 2020 A key aspect of the article by Lousdal on instrumental variables was a discussion of the monotonicity assumption. However, there was no mention of the history of the development of this assumption.
Stuart G. Baker
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Identification and Estimation of Local Average Treatment Effects [PDF]
Econometrica, 1994 We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and
Joshua D. Angrist, Guido W. Imbens
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Individualized treatment rules under stochastic treatment cost constraints
Journal of Causal Inference, 2022 Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods.
Qiu Hongxiang +2 more
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Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates
Entropy, 2020 Estimating the effects of an intervention from high-dimensional observational data is a challenging problem due to the existence of confounding. The task is often further complicated in healthcare applications where a set of observations may be entirely ...
Sonali Parbhoo +3 more
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Conditional average treatment effect estimation with marginally constrained models
Journal of Causal Inference, 2023 Treatment effect estimates are often available from randomized controlled trials as a single average treatment effect for a certain patient population.
van Amsterdam Wouter A. C. +1 more
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Frontiers in Sustainable Food Systems, 2022 Drought is one of the major abiotic constraints that adversely affect maize production in the rain-fed agro-environment in the Asian tropics. In view of the recurrent drought, stress-resilient (SR) maize hybrids were developed and deployed to minimize ...
Atul P. Kulkarni +4 more
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Mathematics, 2022 Regression adjustment is often used to estimate average treatment effect (ATE) in randomized experiments. Recently, some penalty-based regression adjustment methods have been proposed to handle the high-dimensional problem.
Zeyu Diao +3 more
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Causal effect on a target population: A sensitivity analysis to handle missing covariates
Journal of Causal Inference, 2022 Randomized controlled trials (RCTs) are often considered the gold standard for estimating causal effect, but they may lack external validity when the population eligible to the RCT is substantially different from the target population.
Colnet Bénédicte +3 more
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Estimation of Average Treatment Effects with Misclassification [PDF]
Econometrica, 2007 This paper considers identification and estimation of the effect of a mismeasured binary regressor in a nonparametric or semiparametric regression, or the conditional average effect of a binary treatment or policy on some outcome where treatment may be misclassified.
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