Quantifying and reducing inequity in average treatment effect estimation [PDF]
BMC Medical Research Methodology, 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.
Kenneth J. Nieser, Amy L. Cochran
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The functional average treatment effect
Journal of Causal InferenceThis 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
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An improved multiply robust estimator for the average treatment effect [PDF]
BMC Medical Research Methodology, 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,
Ce Wang +4 more
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Estimation of average treatment effect based on a multi-index propensity score [PDF]
BMC Medical Research Methodology, 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 ...
Jiaqin Xu +7 more
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Average Treatment Effect Estimation Via Random Recursive Partitioning
SSRN Electronic Journal, 2004 A new matching method is proposed for the estimation of the average treatment effect of social policy interventions (e.g., training programs or health care measures). Given an outcome variable, a treatment and a set of pre-treatment covariates, the method is based on the examination of random recursive partitions of the space of covariates using ...
Iacus, Stefano, Porro, Giuseppe
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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 +3 more
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Evaluating effectiveness of payments for forest ecosystem services by propensity scores analysis [PDF]
Ekonomika Poljoprivrede (1979), 2020 The Vietnamese Government have been implementing the Payment for Forest Ecosystem Service (PFES) since 2008 with the aim of both improving natural forest status and enhancing income for mountainous community.
Nguyen Huynh Tan, Hung Nguyen Hoang
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Personalized decision making – A conceptual introduction
Journal of Causal Inference, 2023 Personalized decision making targets the behavior of a specific individual, while population-based decision making concerns a subpopulation resembling that individual.
Mueller Scott, Pearl Judea
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Analysis of a Targeted Intervention Programme on the Risk Behaviours of Injecting Drug Users in India: Evidence From the National Integrated Biological and Behavioural Surveillance Survey [PDF]
Journal of Preventive Medicine and Public Health, 2022 Objectives This study provides insights on the impact of a targeted intervention (TI) programme on behaviour change among injecting drug users (IDUs) in India.
Damodar Sahu +6 more
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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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