Results 31 to 40 of about 2,029,988 (242)
Journal of Causal Inference, 2015 Failure (or success) in finding a statistically significant effect of a large-scale intervention may be due to choices made in the evaluation. To highlight the potential limitations and pitfalls of some common identification strategies used for ...
Weber Ann M. +2 more
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Average treatment effects on binary outcomes with stochastic covariates
British Journal of Mathematical and Statistical Psychology, 2023 Abstract When evaluating the effect of psychological treatments on a dichotomous outcome variable in a randomized controlled trial (RCT), covariate adjustment using logistic regression models is often applied. In the presence of covariates, average marginal effects (AMEs) are often preferred over odds ratios, as AMEs yield a clearer ...
Christoph Kiefer +3 more
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Algorithms, 2023 Acute myeloid leukemia (AML) is a type of blood cancer that affects both adults and children. Benzene exposure has been reported to increase the risk of developing AML in children.
Daniela Galatro +7 more
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From Sample Average Treatment Effect to Population Average Treatment Effect on the Treated: Combining Experimental with Observational Studies to Estimate Population Treatment Effects [PDF]
Journal of the Royal Statistical Society Series A: Statistics in Society, 2015 SummaryRandomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive the assumptions that are required to identify population average treatment effects from RCTs.
Hartman, Erin +3 more
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Sichuan jingshen weisheng, 2022 The purpose of the paper was to introduce how to reasonably carry out multiple Logistic regression analysis combined with the average treatment effect analysis.
Hu Chunyan, Hu Liangping
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Sensitivity Analysis for Average Treatment Effects [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2007 Based on the conditional independence or unconfoundedness assumption, matching has become a popular approach to estimate average treatment effects. 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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Efficiency of Average Treatment Effect Estimation When the True Propensity Is Parametric
Econometrics, 2019 It is well known that efficient estimation of average treatment effects can be obtained by the method of inverse propensity score weighting, using the estimated propensity score, even when the true one is known.
Kyoo il Kim
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Dynamic Local Average Treatment Effects
CoRRWe consider Dynamic Treatment Regimes (DTRs) with One Sided Noncompliance that arise in applications such as digital recommendations and adaptive medical trials. These are settings where decision makers encourage individuals to take treatments over time, but adapt encouragements based on previous encouragements, treatments, states, and outcomes ...
Ravi B. Sojitra, Vasilis Syrgkanis
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Debiased Bayesian inference for average treatment effects
CoRR, 2019 NeurIPS ...
Ray, Kolyan, Szabo, Botond
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Propensity Score Matching: should we use it in designing observational studies?
BMC Medical Research MethodologyBackground Propensity Score Matching (PSM) stands as a widely embraced method in comparative effectiveness research. PSM crafts matched datasets, mimicking some attributes of randomized designs, from observational data.
Fei Wan
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