Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization
Fuente:
arXiv
Saved in:
| Main Authors: | Fernández-Loría, Carlos, Loría, Jorge |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects
by: Fernández-Loría, Carlos, et al.
Published: (2025)
by: Fernández-Loría, Carlos, et al.
Published: (2025)
Causal Inference Isn't Special: Why It's Just Another Prediction Problem
by: Fernández-Loría, Carlos
Published: (2025)
by: Fernández-Loría, Carlos
Published: (2025)
Honesty in Causal Forests: When It Helps and When It Hurts
by: Hou, Yanfang, et al.
Published: (2025)
by: Hou, Yanfang, et al.
Published: (2025)
Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings
by: Mäkinen, Lotta, et al.
Published: (2026)
by: Mäkinen, Lotta, et al.
Published: (2026)
Causal Post-Processing of Predictive Models
by: Fernández-Loría, Carlos, et al.
Published: (2024)
by: Fernández-Loría, Carlos, et al.
Published: (2024)
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance
by: Loría, Jorge, et al.
Published: (2023)
by: Loría, Jorge, et al.
Published: (2023)
Deep Kernel Posterior Learning under Infinite Variance Prior Weights
by: Loría, Jorge, et al.
Published: (2024)
by: Loría, Jorge, et al.
Published: (2024)
Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs
by: Björkman, Zachris, et al.
Published: (2025)
by: Björkman, Zachris, et al.
Published: (2025)
Density Ratio-based Proxy Causal Learning Without Density Ratios
by: Bozkurt, Bariscan, et al.
Published: (2025)
by: Bozkurt, Bariscan, et al.
Published: (2025)
Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference Network
by: Weinstein, Bar, et al.
Published: (2025)
by: Weinstein, Bar, et al.
Published: (2025)
Partial Identification of Causal Effects Using Proxy Variables
by: Ghassami, AmirEmad, et al.
Published: (2023)
by: Ghassami, AmirEmad, et al.
Published: (2023)
Identifying Causal Effects Using a Single Proxy Variable
by: Vollmer, Silvan, et al.
Published: (2026)
by: Vollmer, Silvan, et al.
Published: (2026)
Estimating Aleatoric Uncertainty in the Causal Treatment Effect
by: Xu, Liyuan, et al.
Published: (2026)
by: Xu, Liyuan, et al.
Published: (2026)
Transferring Causal Effects using Proxies
by: Iglesias-Alonso, Manuel, et al.
Published: (2025)
by: Iglesias-Alonso, Manuel, et al.
Published: (2025)
Considerations for Estimating Causal Effects of Informatively Timed Treatments
by: Oganisian, Arman
Published: (2025)
by: Oganisian, Arman
Published: (2025)
Fast Proxy Experiment Design for Causal Effect Identification
by: Elahi, Sepehr, et al.
Published: (2024)
by: Elahi, Sepehr, et al.
Published: (2024)
Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery
by: Wang, Zilong, et al.
Published: (2025)
by: Wang, Zilong, et al.
Published: (2025)
Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation
by: Papakostas, Demetrios, et al.
Published: (2024)
by: Papakostas, Demetrios, et al.
Published: (2024)
From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
by: Gutman, Rom, et al.
Published: (2025)
by: Gutman, Rom, et al.
Published: (2025)
Comparing Two Proxy Methods for Causal Identification
by: Guo, Helen, et al.
Published: (2025)
by: Guo, Helen, et al.
Published: (2025)
Causal Information Prioritization for Efficient Reinforcement Learning
by: Cao, Hongye, et al.
Published: (2025)
by: Cao, Hongye, et al.
Published: (2025)
A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
by: Wu, Ying, et al.
Published: (2023)
by: Wu, Ying, et al.
Published: (2023)
Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation
by: Pros, Roger, et al.
Published: (2024)
by: Pros, Roger, et al.
Published: (2024)
A Distributionally Robust Framework for Nuisance in Causal Effect Estimation
by: Tanimoto, Akira
Published: (2025)
by: Tanimoto, Akira
Published: (2025)
Causal Rule Forest: Toward Interpretable and Precise Treatment Effect Estimation
by: Hsu, Chan, et al.
Published: (2024)
by: Hsu, Chan, et al.
Published: (2024)
Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
by: Bozkurt, Bariscan, et al.
Published: (2026)
by: Bozkurt, Bariscan, et al.
Published: (2026)
Density Ratio-Free Doubly Robust Proxy Causal Learning
by: Bozkurt, Bariscan, et al.
Published: (2025)
by: Bozkurt, Bariscan, et al.
Published: (2025)
Dynamic Causal Structure Discovery and Causal Effect Estimation
by: Wang, Jianian, et al.
Published: (2025)
by: Wang, Jianian, et al.
Published: (2025)
Learning Causal Orderings for In-Context Tabular Prediction
by: Xu, Sascha, et al.
Published: (2026)
by: Xu, Sascha, et al.
Published: (2026)
Local Causal Discovery for Estimating Causal Effects
by: Gupta, Shantanu, et al.
Published: (2023)
by: Gupta, Shantanu, et al.
Published: (2023)
A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimators
by: Guo, Jiayi, et al.
Published: (2025)
by: Guo, Jiayi, et al.
Published: (2025)
Causal Discovery via Conditional Independence Testing with Proxy Variables
by: Liu, Mingzhou, et al.
Published: (2023)
by: Liu, Mingzhou, et al.
Published: (2023)
I See, Therefore I Do: Estimating Causal Effects for Image Treatments
by: Thorat, Abhinav, et al.
Published: (2024)
by: Thorat, Abhinav, et al.
Published: (2024)
Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing
by: Nagalapatti, Lokesh, et al.
Published: (2024)
by: Nagalapatti, Lokesh, et al.
Published: (2024)
Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates
by: Wang, Baozhen, et al.
Published: (2025)
by: Wang, Baozhen, et al.
Published: (2025)
Ordering-Based Causal Discovery for Linear and Nonlinear Relations
by: Xu, Zhuopeng, et al.
Published: (2024)
by: Xu, Zhuopeng, et al.
Published: (2024)
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
by: Xu, Liyuan, et al.
Published: (2021)
by: Xu, Liyuan, et al.
Published: (2021)
Cross-Treatment Effect Estimation for Multi-Category, Multi-Valued Causal Inference via Dynamic Neural Masking
by: Ke, Xiaopeng, et al.
Published: (2025)
by: Ke, Xiaopeng, et al.
Published: (2025)
Causal Machine Learning Methods for Estimating Personalised Treatment Effects -- Insights on validity from two large trials
by: Chen, Hongruyu, et al.
Published: (2025)
by: Chen, Hongruyu, et al.
Published: (2025)
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
by: Balazadeh, Vahid, et al.
Published: (2025)
by: Balazadeh, Vahid, et al.
Published: (2025)
Similar Items
-
The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects
by: Fernández-Loría, Carlos, et al.
Published: (2025) -
Causal Inference Isn't Special: Why It's Just Another Prediction Problem
by: Fernández-Loría, Carlos
Published: (2025) -
Honesty in Causal Forests: When It Helps and When It Hurts
by: Hou, Yanfang, et al.
Published: (2025) -
Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings
by: Mäkinen, Lotta, et al.
Published: (2026) -
Causal Post-Processing of Predictive Models
by: Fernández-Loría, Carlos, et al.
Published: (2024)