Integrating Copula-Based Random Forest and Deep Learning Approaches for Analyzing Heterogeneous Treatment Effects in Survival Analysis

Integrating Copula-Based Random Forest and Deep Learning Approaches for Analyzing Heterogeneous Treatment Effects in Survival Analysis

This paper presents deep learning models—specifically, Long Short-Term Memory (LSTM) networks and hybrid Convolutional Neural Network–LSTM (CNN-LSTM) with a Copula-Based Random Forest (CBRF) model to estimate Heterogeneous Treatment Effects (HTEs) in survival analysis. The proposed method is designe...

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Bibliographic Details
Main Author: Jong-Min Kim
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Mathematics
Subjects:
causal inference
deep learning
copula
survival analysis
Online Access:https://www.mdpi.com/2227-7390/13/10/1659
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