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Erratum to: FoCAT: Foundation Model for Estimating the Conditional Average Treatment Effect

Doklady Mathematics
An Erratum to this paper has been published: https://doi.org/10.1134 ...
S. Kirpichenko, A. Konstantinov, L. V. Utkin   +2 more
semanticscholar   +2 more sources

Marginal and Conditional Importance Measures from Machine Learning Models and Their Relationship with Conditional Average Treatment Effect

CoRR
Interpreting black-box machine learning models is challenging due to their strong dependence on data and inherently non-parametric nature. This paper reintroduces the concept of importance through"Marginal Variable Importance Metric"(MVIM), a model ...
M. Khan, Olli Saarela, Rafal Kustra
semanticscholar   +4 more sources

Estimating Conditional Average Treatment Effects with Missing Treatment Information

CoRR, 2022
Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention. In this paper, we analyze CATE estimation in the setting with missing treatments where unique challenges arise
Kuzmanovic, Milan, Hatt, Tobias, Feuerriegel, Stefan   +2 more
openaire   +4 more sources

Estimation of Conditional Average Treatment Effects With High-Dimensional Data [PDF]

Journal of Business & Economic Statistics, 2020
Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the ...
FAN, Qingliang, HSU, Yu-Chin, LIELI, Robert P., ZHANG, Yichong   +3 more
openaire   +3 more sources

Personalized decision making – A conceptual introduction

Journal of Causal Inference, 2023
Personalized decision making targets the behavior of a specific individual, while population-based decision making concerns a subpopulation resembling that individual.
Mueller Scott, Pearl Judea
doaj   +1 more source

Estimating Conditional Average Treatment Effects [PDF]

Journal of Business & Economic Statistics, 2014
We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture the heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined in terms of the possible values of a set of continuous ...
Jason Abrevaya, Yu-Chin Hsu, Robert P. Lieli   +2 more
openaire   +1 more source

Estimation of partially conditional average treatment effect by double kernel-covariate balancing [PDF]

Electronic Journal of Statistics, 2021
We study nonparametric estimation for the partially conditional average treatment effect, defined as the treatment effect function over an interested subset of confounders.
Jiayi Wang, Raymond K. W. Wong, Shu Yang, K. C. G. Chan   +3 more
semanticscholar   +1 more source

Optimal weighting for estimating generalized average treatment effects

Journal of Causal Inference, 2022
In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects.
Kallus Nathan, Santacatterina Michele
doaj   +1 more source

Heterogeneous interventional effects with multiple mediators: Semiparametric and nonparametric approaches

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, Branson Zach, Kennedy Edward H.   +2 more
doaj   +1 more source

Robust Inference of Conditional Average Treatment Effects Using Dimension Reduction

Statistica Sinica, 2023
It is important to make robust inference of the conditional average treatment effect from observational data, but this becomes challenging when the confounder is multivariate or high-dimensional. In this article, we propose a double dimension reduction method, which reduces the curse of dimensionality as much as possible while keeping the nonparametric
Huang, Ming-Yueh, Yang, Shu
openaire   +4 more sources