Results 11 to 20 of about 4,712,566 (254)

Conditional average treatment effect estimation with marginally constrained models [PDF]

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., Ranganath Rajesh   +1 more
doaj   +2 more sources

Quantifying and reducing inequity in average treatment effect estimation. [PDF]

BMC Med Res Methodol, 2023
Background Across studies of average treatment effects, some population subgroups consistently have lower representation than others which can lead to discrepancies in how well results generalize.
Nieser KJ, Cochran AL.
europepmc   +2 more sources

Comparison of Propensity Score Weighting Methods to Remove Selection Bias in Average Treatment Effect Estimates

Uluslararası Türk Eğitim Bilimleri Dergisi, 2023
In this Monte Carlo simulation study, the performance of six different propensity score methods implemented through weighting cases was investigated: inverse probability of treatment weighting, truncated inverse probability of treatment weighting ...
Sungur Gürel, Walter Lana Leite
doaj   +2 more sources

The functional average treatment effect

Journal of Causal Inference
This article establishes the functional average as an important estimand for causal inference. The significance of the estimand lies in its robustness against traditional issues of confounding.
Sparkes Shane, Garcia Erika, Zhang Lu
doaj   +3 more sources

An improved multiply robust estimator for the average treatment effect. [PDF]

BMC Med Res Methodol, 2023
Background In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the average treatment effect (ATE). However,
Wang C, Wei K, Huang C, Yu Y, Qin G.
europepmc   +2 more sources

Estimation of average treatment effect based on a multi-index propensity score. [PDF]

BMC Med Res Methodol, 2022
Background Estimating the average effect of a treatment, exposure, or intervention on health outcomes is a primary aim of many medical studies. However, unbalanced covariates between groups can lead to confounding bias when using observational data to ...
Xu J, Wei K, Wang C, Huang C, Xue Y, Zhang R, Qin G, Yu Y.   +7 more
europepmc   +2 more sources

Adaptive-TMLE for the average treatment effect based on randomized controlled trial augmented with real-world data

Journal of Causal Inference
We consider the problem of estimating the average treatment effect (ATE) when both randomized control trial (RCT) data and external real-world data (RWD) are available.
van der Laan Mark, Qiu Sky, Tarp Jens Magelund, van der Laan Lars   +3 more
doaj   +2 more sources

Doubly robust nonparametric inference on the average treatment effect. [PDF]

Biometrika, 2017
Summary Doubly robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double robustness ...
Benkeser D, Carone M, Laan MJV, Gilbert PB.   +3 more
europepmc   +4 more sources

Corrigendum: A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R. [PDF]

Front Psychol, 2020
Shi D, Tong X, Meyer MJ.
europepmc   +3 more sources

Approaches to the Estimation of the Local Average Treatment Effect in a Regression Discontinuity Design. [PDF]

Scand Stat Theory Appl, 2016
O'Keeffe AG, Baio G.
europepmc   +3 more sources