Results 11 to 20 of about 23,676,347 (287)
mediation: R Package for Causal Mediation Analysis [PDF]
Journal of Statistical Software, 2014 In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding
Dustin Tingley +4 more
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Disentangled Representation for Causal Mediation Analysis [PDF]
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Estimating direct and indirect causal effects from observational data is crucial to understanding the causal mechanisms and predicting the behaviour under different interventions. Causal mediation analysis is a method that is often used to reveal direct and indirect effects.
Ziqi Xu 0001 +5 more
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This two-day seminar provides a comprehensive exploration of causal mediation analysis, emphasizing strong theoretical foundations and practical applications for researchers in diverse fields. Participants will gain crucial skills in implementing these advanced methodologies using R and Stata, enhancing the credibility and impact of their research ...
Paola Zaninotto, Martin Danka
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Structural Equation Modeling: A Multidisciplinary Journal, 2022 Although causal mediation analysis clarifies causal effect estimation, little attention has been devoted to the differences between causal estimation approaches. This paper illustrates the difference between the causal estimation approaches for mediation models with a binary mediator.
Noah A. Schuster +3 more
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A novel causal mediation analysis approach for zero‐inflated mediators
Statistics in Medicine, 2023 Mediation analyses play important roles in making causal inference in biomedical research to examine causal pathways that may be mediated by one or more intermediate variables (ie, mediators). Although mediation frameworks have been well established such as counterfactual‐outcomes (ie, potential‐outcomes) models and traditional linear mediation models,
Meilin Jiang +4 more
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Causal Mediation Analysis with Spatial Interference
In the causal inference framework, estimating causal effects requires specific assumptions, among which is the assumption of independence of each unit’s outcome from the treatment assigned to other units.
Di Maria, C., Adelfio, G.
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Causal Mediation Analysis with Multiple Mediators [PDF]
Biometrics, 2014 Summary In diverse fields of empirical research—including many in the biological sciences—attempts are made to decompose the effect of an exposure on an outcome into its effects via a number of different pathways. For example, we may wish to separate the effect of heavy alcohol consumption on systolic blood pressure (SBP) into effects ...
Daniel, RM +3 more
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Generalized Causal Mediation Analysis [PDF]
Biometrics, 2011 The goal of mediation analysis is to assess direct and indirect effects of a treatment or exposure on an outcome. More generally, we may be interested in the context of a causal model as characterized by a directed acyclic graph (DAG), where mediation via a specific path from exposure to outcome may involve an arbitrary number of links (or "stages ...
Albert, Jeffrey M., Nelson, Suchitra
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Causal Mediation Analysis with Hidden Confounders [PDF]
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 2022 An important problem in causal inference is to break down the total effect of a treatment on an outcome into different causal pathways and to quantify the causal effect in each pathway. For instance, in causal fairness, the total effect of being a male employee (i.e., treatment) constitutes its direct effect on annual income (i.e., outcome) and the ...
Lu Cheng 0001 +2 more
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Causal mediation analysis with a latent mediator [PDF]
Biometrical Journal, 2015 Health researchers are often interested in assessing the direct effect of a treatment or exposure on an outcome variable, as well as its indirect (or mediation) effect through an intermediate variable (or mediator). For an outcome following a nonlinear model, the mediation formula may be used to estimate causally interpretable mediation effects.
Albert, Jeffrey M. +2 more
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