Results 1 to 10 of about 252 (167)

An approach to nonparametric inference on the causal dose–response function [PDF]

Journal of Causal Inference
The causal dose–response curve is commonly selected as the statistical parameter of interest in studies where the goal is to understand the effect of a continuous exposure on an outcome.
Hudson Aaron, Geng Elvin H., Odeny Thomas A., Bukusi Elizabeth A., Petersen Maya L., van der Laan Mark J.   +5 more
doaj   +2 more sources

Semiparametric discovery and estimation of interaction in mixed exposures using stochastic interventions [PDF]

Journal of Causal Inference
Understanding 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., Hubbard Alan, van der Laan Mark, Schuler Alejandro   +3 more
doaj   +2 more sources

Complete consistency for the estimator of nonparametric regression model based on m-END errors

Open Mathematics, 2021
In this paper, we study the complete consistency for the estimator of nonparametric regression model based on m-END errors and obtain the convergence rates of the complete consistency under more general conditions.
Zhang Shui-Li, Hou Tiantian, Qu Cong
doaj   +1 more source

A fundamental measure of treatment effect heterogeneity

Journal of Causal Inference, 2021
The stratum-specific treatment effect function is a random variable giving the average treatment effect (ATE) for a randomly drawn stratum of potential confounders a clinician may use to assign treatment.
Levy Jonathan, van der Laan Mark, Hubbard Alan, Pirracchio Romain   +3 more
doaj   +1 more source

Identification of causal intervention effects under contagion

Journal of Causal Inference, 2021
Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment – such as a vaccine – given to one individual may affect the infection outcomes of others. Epidemiologists have proposed
Cai Xiaoxuan, Loh Wen Wei, Crawford Forrest W.   +2 more
doaj   +1 more source

Explaining predictive models using Shapley values and non-parametric vine copulas

Dependence Modeling, 2021
In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values.
Aas Kjersti, Nagler Thomas, Jullum Martin, Løland Anders   +3 more
doaj   +1 more source

Robust estimation for varying coefficient partially functional linear regression models based on exponential squared loss function

Open Mathematics, 2022
In this article, we present a new robust estimation procedure based on the exponential squared loss function for varying coefficient partially functional linear regression models, where the slope function and nonparametric coefficients are approximated ...
Sun Jun, Liu Wanrong
doaj   +1 more source

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
doaj   +1 more source

Incremental intervention effects in studies with dropout and many timepoints#

Journal of Causal Inference, 2021
Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations.
Kim Kwangho, Kennedy Edward H., Naimi Ashley I.   +2 more
doaj   +1 more source

Statistics of Extremes under Random Censoring [PDF]

, 2006
AMS classifications: 62G05; 62G20; 62G32 ...
John H. J. Einmahl, Amélie Fils-Villetard, Einmahl, J.H.J., Guillou, A., Fils-Villetard, A., Armelle Guillou   +5 more
core   +1 more source