publication . Preprint . 2016

Efficient nonparametric estimation of causal mediation effects

Chan, K. C. G.; Imai, K.; Yam, S. C. P.; Zhang, Z.;
Open Access English
  • Published: 14 Jan 2016
Abstract
An essential goal of program evaluation and scientific research is the investigation of causal mechanisms. Over the past several decades, causal mediation analysis has been used in medical and social sciences to decompose the treatment effect into the natural direct and indirect effects. However, all of the existing mediation analysis methods rely on parametric modeling assumptions in one way or another, typically requiring researchers to specify multiple regression models involving the treatment, mediator, outcome, and pre-treatment confounders. To overcome this limitation, we propose a novel nonparametric estimation method for causal mediation analysis that el...
Subjects
free text keywords: Statistics - Methodology, 62G05
Funded by
NSF| Statistical Analysis of Causal Mechanisms: Identification, Inference, and Sensitivity Analysis
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 0918968
  • Funding stream: Directorate for Social, Behavioral & Economic Sciences | Division of Social and Economic Sciences
,
NIH| Methods of reduced-cost designs for studying disease incidence and fatality risks
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01HL122212-01
  • Funding stream: NATIONAL HEART, LUNG, AND BLOOD INSTITUTE
,
NIH| Statistical Methods for Adherence Issues in HIV Prevention Research
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01AI121259-01
  • Funding stream: NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
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Assumption 6. The function E(Y | T = 1, M = m, X = x) is t-times jointly continuously differentiable with respect to (x, m), and η(1, 0, x) is d′- times continuously differentiable w.r.t. x, where d > 3r/2 and d′ > 3r1/2.

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