Results 231 to 240 of about 18,931,441 (264)

Causal Temporal Neural Networks Using the Conditional Average Treatment Effect

2025 IEEE Conference on Artificial Intelligence (CAI)
This paper presents a method to integrate causal inference into deep learning for time series forecasting. We consider time series for complex systems characterized by non-linear dynamics, high dimensionality, and uncertainty.
Shahenda Youssef, Julius Pfrommer, Jürgen Beyerer   +2 more
semanticscholar   +3 more sources

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Estimation of conditional average treatment effect by covariates balance methods

Statistics and Its Interface, 2022
Jun Wang, Chang-Biao Liu
semanticscholar   +2 more sources

CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression

arXiv.org, 2023
In causal inference about two treatments, Conditional Average Treatment Effects (CATEs) play an important role as a quantity representing an individualized causal effect, defined as a difference between the expected outcomes of the two treatments ...
Masahiro Kato, Masaaki Imaizumi
semanticscholar   +1 more source

Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation

arXiv.org
Numerous algorithms have been developed for Conditional Average Treatment Effect (CATE) estimation. In this paper, we first highlight a common issue where many algorithms exhibit inconsistent learning behavior for the same instance across different group
Yi-Fu Fu, Keng-Te Liao, Shou-De Lin
semanticscholar   +1 more source

Multi-CATE: Multi-Accurate Conditional Average Treatment Effect Estimation Robust to Unknown Covariate Shifts

arXiv.org
Estimating heterogeneous treatment effects is important to tailor treatments to those individuals who would most likely benefit. However, conditional average treatment effect predictors may often be trained on one population but possibly deployed on ...
Christoph Kern, Michael Kim, Angela Zhou
semanticscholar   +1 more source

Weighted Estimation of Conditional Average Treatment Effect Function With Adjusted Covariate Mismeasurement

Stat
Causal inference continues to be a significant mainstay in various fields, and methods have been developed to capture the variability of treatment effects among subpopulations in observational studies.
Yassin Nshimiyimana, Yuqi Qiu, Yingchun Zhou   +2 more
semanticscholar   +1 more source

Denoised Conformal Alignment for Reliable Selection of Conditional Average Treatment Effect Predictions

arXiv.org
In selective deployment, practitioners act only on a model-chosen subset of individuals based on predicted conditional average treatment effects, but marginal conformal guarantees need not control reliability on that selected subset.
Xin Lu, Hao-Ang Chi, Zhi-Heng Zhang
semanticscholar   +1 more source

Conditional Average Treatment Effects and Decision Making

2014
This paper develops a decision making empirical method to evaluate welfare programs accounting for heterogeneity of impacts. We find outcome predictive distributions for different subgroups of the population and use a characterization of second order stochastic dominance to give a policy recommendation conditional on covariates with minimal ...
openaire   +1 more source

Relaxing conditions for local average treatment effect in fuzzy regression discontinuity

Economics Letters, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Choi, Jin-young, Lee, Myoung-jae
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Significance test for semiparametric conditional average treatment effects and other structural functions

Computational Statistics & Data Analysis
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Niwen Zhou, Xu Guo, Lixing Zhu
openaire   +2 more sources