Results 1 to 10 of about 936 (117)
Discovery of critical thresholds in mixed exposures and estimation of policy intervention effects [PDF]
Journal of Causal InferenceRegulations of chemical exposures often focus on individual substances, neglecting the amplified toxicity that can arise from multiple concurrent exposures.
McCoy David B. +3 more
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Semiparametric discovery and estimation of interaction in mixed exposures using stochastic interventions [PDF]
Journal of Causal InferenceUnderstanding the complex interactions among multiple environmental exposures is critical for assessing their combined impact on health outcomes. This study introduces InterXshift, a novel semiparametric method that provides a nonparametric definition of
McCoy David B. +3 more
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Orthogonal prediction of counterfactual outcomes [PDF]
Journal of Causal InferenceOrthogonal meta-learners, such as DR-learner (Kennedy EH. Towards optimal doubly robust estimation of heterogeneous causal effects. arXiv preprint arXiv:2004.14497 2020), R-learner (Nie X, Wager S.
Vansteelandt Stijn, Morzywołek Paweł
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Application of one-step method to parameter estimation in ODE models. [PDF]
Stat Neerl, 2018 In this paper, we study application of Le Cam's one‐step method to parameter estimation in ordinary differential equation models. This computationally simple technique can serve as an alternative to numerical evaluation of the popular non‐linear least squares estimator, which typically requires the use of a multistep iterative algorithm and repetitive ...
Dattner I, Gugushvili S.
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Dependence Modeling, 2021 Consider a survival time study, where a sequence of possibly censored failure times is observed with d-dimensional covariate The main goal of this article is to establish the asymptotic normality of the kernel estimator of the relative error regression ...
Bouhadjera Feriel, Saïd Elias Ould
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Journal of Causal Inference, 2023 We propose semiparametric and nonparametric methods to estimate conditional interventional indirect effects in the setting of two discrete mediators whose causal ordering is unknown. Average interventional indirect effects have been shown to decompose an
Rubinstein Max +2 more
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Nonparametric inference for interventional effects with multiple mediators
Journal of Causal Inference, 2021 Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway-specific effects.
Benkeser David, Ran Jialu
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Classical Testing in Functional Linear Models. [PDF]
J Nonparametr Stat, 2016 Kong D, Staicu AM, Maity A.
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Instrumental variable regression via kernel maximum moment loss
Journal of Causal Inference, 2023 We investigate a simple objective for nonlinear instrumental variable (IV) regression based on a kernelized conditional moment restriction known as a maximum moment restriction (MMR).
Zhang Rui +3 more
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Nonparametric C- and D-vine-based quantile regression
Dependence Modeling, 2022 Quantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional quantile functions provides more accurate modeling of the stochastic ...
Tepegjozova Marija +3 more
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