A Copula-Driven CNN-LSTM Framework for Estimating Heterogeneous Treatment Effects in Multivariate Outcomes

A Copula-Driven CNN-LSTM Framework for Estimating Heterogeneous Treatment Effects in Multivariate Outcomes

Estimating heterogeneous treatment effects (HTEs) across multiple correlated outcomes poses significant challenges due to complex dependency structures and diverse data types. In this study, we propose a novel deep learning framework integrating empirical copula transformations with a CNN-LSTM (Conv...

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Bibliographic Details
Main Author: Jong-Min Kim
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Mathematics
Subjects:
heterogeneous treatment effects
empirical copula
CNN-LSTM
multitask learning
causal inference
survival analysis
Online Access:https://www.mdpi.com/2227-7390/13/15/2384
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