Results 1 to 10 of about 269 (169)

Mediated probabilities of causation [PDF]

Journal of Causal Inference
We propose a set of causal estimands that we call “the mediated probabilities of causation.” These estimands quantify the probabilities that an observed negative outcome was induced via a mediating pathway versus a direct pathway in a stylized setting ...
Rubinstein Max, Cuellar Maria, Malinsky Daniel   +2 more
doaj   +2 more sources

Bounding causal effects with an unknown mixture of informative and non-informative missingness [PDF]

Journal of Causal Inference
In experimental and observational data settings, researchers often have limited knowledge of the reasons for missing outcomes. To address this uncertainty, we propose bounds on causal effects for missing outcomes, accommodating the scenario where ...
Rubinstein Max, Agniel Denis, Han Larry, Horvitz-Lennon Marcela, Normand Sharon-Lise   +4 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

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

HAL-based plug-in estimation with pointwise asymptotic normality of the causal dose–response curve [PDF]

Journal of Causal Inference
Estimating and obtaining reliable inference for the marginally adjusted causal dose–response curve for continuous treatments without relying on parametric assumptions is a well-known statistical challenge.
Shi Junming, Zhang Wenxin, Hubbard Alan E., van der Laan Mark J.   +3 more
doaj   +2 more sources

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

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