Causal Rule Forest: Toward Interpretable and Precise Treatment Effect Estimation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Hsu, Chan, Wu, Jun-Ting, Kang, Yihuang |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models
par: Li, Chih-Yuan, et autres
Publié: (2024)
par: Li, Chih-Yuan, et autres
Publié: (2024)
LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference
par: Lee, Po-Han, et autres
Publié: (2025)
par: Lee, Po-Han, et autres
Publié: (2025)
Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework
par: Wu, Peng-Yi, et autres
Publié: (2025)
par: Wu, Peng-Yi, et autres
Publié: (2025)
Subgroup Analysis via Model-based Rule Forest
par: Cheng, I-Ling, et autres
Publié: (2024)
par: Cheng, I-Ling, et autres
Publié: (2024)
Locally Interpretable Individualized Treatment Rules for Black-Box Decision Models
par: Charvadeh, Yasin Khadem, et autres
Publié: (2026)
par: Charvadeh, Yasin Khadem, et autres
Publié: (2026)
I See, Therefore I Do: Estimating Causal Effects for Image Treatments
par: Thorat, Abhinav, et autres
Publié: (2024)
par: Thorat, Abhinav, et autres
Publié: (2024)
Estimating Causal Effects from Learned Causal Networks
par: Raichev, Anna, et autres
Publié: (2024)
par: Raichev, Anna, et autres
Publié: (2024)
Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks
par: Patil, Gandharv, et autres
Publié: (2026)
par: Patil, Gandharv, et autres
Publié: (2026)
From Black-box to Causal-box: Towards Building More Interpretable Models
par: Hwang, Inwoo, et autres
Publié: (2025)
par: Hwang, Inwoo, et autres
Publié: (2025)
Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
par: Cho, Dongchan, et autres
Publié: (2025)
par: Cho, Dongchan, et autres
Publié: (2025)
Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders
par: Job, Simi, et autres
Publié: (2026)
par: Job, Simi, et autres
Publié: (2026)
Conformal Prediction for Causal Effects of Continuous Treatments
par: Schröder, Maresa, et autres
Publié: (2024)
par: Schröder, Maresa, et autres
Publié: (2024)
Interpretable Hybrid-Rule Temporal Point Processes
par: Cao, Yunyang, et autres
Publié: (2025)
par: Cao, Yunyang, et autres
Publié: (2025)
Interpretable Representation Learning for Additive Rule Ensembles
par: Behzadimanesh, Shahrzad, et autres
Publié: (2025)
par: Behzadimanesh, Shahrzad, et autres
Publié: (2025)
Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes
par: Chauhan, Vinod Kumar, et autres
Publié: (2025)
par: Chauhan, Vinod Kumar, et autres
Publié: (2025)
Compositional Models for Estimating Causal Effects
par: Pruthi, Purva, et autres
Publié: (2024)
par: Pruthi, Purva, et autres
Publié: (2024)
Surrogate Interpretable Graph for Random Decision Forests
par: Dubey, Akshat, et autres
Publié: (2025)
par: Dubey, Akshat, et autres
Publié: (2025)
Random Rule Forest (RRF): Interpretable Ensembles of LLM-Generated Questions for Predicting Startup Success
par: Griffin, Ben, et autres
Publié: (2025)
par: Griffin, Ben, et autres
Publié: (2025)
Towards Interpretable Deep Generative Models via Causal Representation Learning
par: Moran, Gemma E., et autres
Publié: (2025)
par: Moran, Gemma E., et autres
Publié: (2025)
Disentangled Graph Autoencoder for Treatment Effect Estimation
par: Fan, Di, et autres
Publié: (2024)
par: Fan, Di, et autres
Publié: (2024)
Estimating Treatment Effects with Independent Component Analysis
par: Reizinger, Patrik, et autres
Publié: (2025)
par: Reizinger, Patrik, et autres
Publié: (2025)
Dynamic Interpretability for Model Comparison via Decision Rules
par: Rida, Adam, et autres
Publié: (2023)
par: Rida, Adam, et autres
Publié: (2023)
Learning Interpretable Rules for Scalable Data Representation and Classification
par: Wang, Zhuo, et autres
Publié: (2023)
par: Wang, Zhuo, et autres
Publié: (2023)
Treatment Effect Estimation with Differentiated Networked Effect on Graph Data
par: Lin, Xiaofeng, et autres
Publié: (2026)
par: Lin, Xiaofeng, et autres
Publié: (2026)
Joint Treatment Effect Estimation from Incomplete Healthcare Data: Temporal Causal Normalizing Flows with LLM-driven Evolutionary MNAR Imputation
par: Parra, Olivia Jullian, et autres
Publié: (2026)
par: Parra, Olivia Jullian, et autres
Publié: (2026)
Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects
par: Wang, Yingrong, et autres
Publié: (2024)
par: Wang, Yingrong, et autres
Publié: (2024)
A Distributionally Robust Framework for Nuisance in Causal Effect Estimation
par: Tanimoto, Akira
Publié: (2025)
par: Tanimoto, Akira
Publié: (2025)
Estimating Causal Effects in Gaussian Linear SCMs with Finite Data
par: Maiti, Aurghya, et autres
Publié: (2026)
par: Maiti, Aurghya, et autres
Publié: (2026)
Disentangled Double Machine Learning for Accurate Causal Effect Estimation
par: Xiang, Guodu, et autres
Publié: (2026)
par: Xiang, Guodu, et autres
Publié: (2026)
Actionable Interpretability via Causal Hypergraphs: Unravelling Batch Size Effects in Deep Learning
par: Sun, Zhongtian, et autres
Publié: (2025)
par: Sun, Zhongtian, et autres
Publié: (2025)
KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs
par: Liu, Ruoqi, et autres
Publié: (2024)
par: Liu, Ruoqi, et autres
Publié: (2024)
Deep Disentangled Representation Network for Treatment Effect Estimation
par: Meng, Hui, et autres
Publié: (2025)
par: Meng, Hui, et autres
Publié: (2025)
PIPCFR: Pseudo-outcome Imputation with Post-treatment Variables for Individual Treatment Effect Estimation
par: Lin, Zichuan, et autres
Publié: (2025)
par: Lin, Zichuan, et autres
Publié: (2025)
The Estimation of Continual Causal Effect for Dataset Shifting Streams
par: Chen, Baining, et autres
Publié: (2025)
par: Chen, Baining, et autres
Publié: (2025)
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
par: Reddy, Abbavaram Gowtham, et autres
Publié: (2022)
par: Reddy, Abbavaram Gowtham, et autres
Publié: (2022)
Bilinear Convolution Decomposition for Causal RL Interpretability
par: Oozeer, Narmeen, et autres
Publié: (2024)
par: Oozeer, Narmeen, et autres
Publié: (2024)
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems
par: Huang, Zijie, et autres
Publié: (2024)
par: Huang, Zijie, et autres
Publié: (2024)
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
par: Huang, Yiyan, et autres
Publié: (2024)
par: Huang, Yiyan, et autres
Publié: (2024)
Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks
par: Cui, Ziqiang, et autres
Publié: (2024)
par: Cui, Ziqiang, et autres
Publié: (2024)
Interpretable Classification via a Rule Network with Selective Logical Operators
par: Wei, Bowen, et autres
Publié: (2024)
par: Wei, Bowen, et autres
Publié: (2024)
Documents similaires
-
Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models
par: Li, Chih-Yuan, et autres
Publié: (2024) -
LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference
par: Lee, Po-Han, et autres
Publié: (2025) -
Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework
par: Wu, Peng-Yi, et autres
Publié: (2025) -
Subgroup Analysis via Model-based Rule Forest
par: Cheng, I-Ling, et autres
Publié: (2024) -
Locally Interpretable Individualized Treatment Rules for Black-Box Decision Models
par: Charvadeh, Yasin Khadem, et autres
Publié: (2026)