Results 1 to 10 of about 278 (169)
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 +3 more
doaj +1 more source
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 +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
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 +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 +2 more
doaj +1 more source
Empirical likelihood for quantile regression models with response data missing at random
Open Mathematics, 2017 This paper studies quantile linear regression models with response data missing at random. A quantile empirical-likelihood-based method is proposed firstly to study a quantile linear regression model with response data missing at random.
Luo S., Pang Shuxia
doaj +1 more source
Minimally capturing heterogeneous complier effect of endogenous treatment for any outcome variable
Journal of Causal Inference, 2023 When a binary treatment DD is possibly endogenous, a binary instrument δ\delta is often used to identify the “effect on compliers.” If covariates XX affect both DD and an outcome YY, XX should be controlled to identify the “XX-conditional complier ...
Lee Goeun +2 more
doaj +1 more source
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
doaj +1 more source