The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects
Fuente:
arXiv
Saved in:
| Main Authors: | Fernández-Loría, Carlos, Loría, Jorge |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization
by: Fernández-Loría, Carlos, et al.
Published: (2022)
by: Fernández-Loría, Carlos, et al.
Published: (2022)
Causal Inference Isn't Special: Why It's Just Another Prediction Problem
by: Fernández-Loría, Carlos
Published: (2025)
by: Fernández-Loría, Carlos
Published: (2025)
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)
Local Causal Discovery for Estimating Causal Effects
by: Gupta, Shantanu, et al.
Published: (2023)
by: Gupta, Shantanu, et al.
Published: (2023)
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)
Causal Effect Estimation with Learned Instrument Representations
by: Dean, Frances, et al.
Published: (2026)
by: Dean, Frances, et al.
Published: (2026)
Considerations for Estimating Causal Effects of Informatively Timed Treatments
by: Oganisian, Arman
Published: (2025)
by: Oganisian, Arman
Published: (2025)
The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation
by: Machlanski, Damian, et al.
Published: (2023)
by: Machlanski, Damian, et al.
Published: (2023)
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
by: Ter-Minassian, Lucile, et al.
Published: (2024)
by: Ter-Minassian, Lucile, et al.
Published: (2024)
Compositional Models for Estimating Causal Effects
by: Pruthi, Purva, et al.
Published: (2024)
by: Pruthi, Purva, et al.
Published: (2024)
Transfer Learning for Causal Effect Estimation
by: Wei, Song, et al.
Published: (2023)
by: Wei, Song, et al.
Published: (2023)
Topological Causal Effects
by: Kim, Kwangho, et al.
Published: (2026)
by: Kim, Kwangho, et al.
Published: (2026)
Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding
by: Hafer, Marlies, et al.
Published: (2025)
by: Hafer, Marlies, et al.
Published: (2025)
Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning
by: Tanaka, Masahiro
Published: (2025)
by: Tanaka, Masahiro
Published: (2025)
Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap
by: Clivio, Oscar, et al.
Published: (2026)
by: Clivio, Oscar, et al.
Published: (2026)
A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects
by: Pham, Khiem, et al.
Published: (2023)
by: Pham, Khiem, et al.
Published: (2023)
RepFlow: Representation Enhanced Flow Matching for Causal Effect Estimation
by: Xie, Yifei, et al.
Published: (2026)
by: Xie, Yifei, et al.
Published: (2026)
Undersmoothing Causal Estimators with Generative Trees
by: Machlanski, Damian, et al.
Published: (2022)
by: Machlanski, Damian, et al.
Published: (2022)
Black Box Causal Inference: Effect Estimation via Meta Prediction
by: Bynum, Lucius E. J., et al.
Published: (2025)
by: Bynum, Lucius E. J., et al.
Published: (2025)
MMCE: A Framework for Deep Monotonic Modeling of Multiple Causal Effects
by: Chen, Juhua, et al.
Published: (2025)
by: Chen, Juhua, et al.
Published: (2025)
Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivity
by: Wiederkehr, Christoph, et al.
Published: (2025)
by: Wiederkehr, Christoph, et al.
Published: (2025)
Causal Estimation of Exposure Shifts with Neural Networks
by: Tec, Mauricio, et al.
Published: (2023)
by: Tec, Mauricio, et al.
Published: (2023)
The Estimation of Continual Causal Effect for Dataset Shifting Streams
by: Chen, Baining, et al.
Published: (2025)
by: Chen, Baining, et al.
Published: (2025)
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
by: Reddy, Abbavaram Gowtham, et al.
Published: (2022)
by: Reddy, Abbavaram Gowtham, et al.
Published: (2022)
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)
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
by: Fuhr, Jonathan, et al.
Published: (2024)
by: Fuhr, Jonathan, et al.
Published: (2024)
Causal Post-Processing of Predictive Models
by: Fernández-Loría, Carlos, et al.
Published: (2024)
by: Fernández-Loría, Carlos, et al.
Published: (2024)
Causal-learn: Causal Discovery in Python
by: Zheng, Yujia, et al.
Published: (2023)
by: Zheng, Yujia, et al.
Published: (2023)
Optimization-based Causal Estimation from Heterogenous Environments
by: Yin, Mingzhang, et al.
Published: (2021)
by: Yin, Mingzhang, et al.
Published: (2021)
A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation
by: Song, Xinran, et al.
Published: (2025)
by: Song, Xinran, et al.
Published: (2025)
A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation
by: Shi, Jieru, et al.
Published: (2023)
by: Shi, Jieru, et al.
Published: (2023)
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
by: Mahajan, Divyat, et al.
Published: (2022)
by: Mahajan, Divyat, et al.
Published: (2022)
Do Finetti: On Causal Effects for Exchangeable Data
by: Guo, Siyuan, et al.
Published: (2024)
by: Guo, Siyuan, et al.
Published: (2024)
Estimating Causal Effects in Networks with Cluster-Based Bandits
by: Faruk, Ahmed Sayeed, et al.
Published: (2025)
by: Faruk, Ahmed Sayeed, et al.
Published: (2025)
Calibrated and Conformal Propensity Scores for Causal Effect Estimation
by: Deshpande, Shachi, et al.
Published: (2023)
by: Deshpande, Shachi, et al.
Published: (2023)
CausalFormer: An Interpretable Transformer for Temporal Causal Discovery
by: Kong, Lingbai, et al.
Published: (2024)
by: Kong, Lingbai, et al.
Published: (2024)
A Latent Causal Inference Framework for Ordinal Variables
by: Scauda, Martina, et al.
Published: (2025)
by: Scauda, Martina, et al.
Published: (2025)
C-HDNet: Hyperdimensional Computing for Causal Effect Estimation from Observational Data Under Network Interference
by: Dalvi, Abhishek, et al.
Published: (2025)
by: Dalvi, Abhishek, et al.
Published: (2025)
Average Causal Effect Estimation in DAGs with Hidden Variables: Beyond Back-Door and Front-Door Criteria
by: Guo, Anna, et al.
Published: (2024)
by: Guo, Anna, et al.
Published: (2024)
Similar Items
-
Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization
by: Fernández-Loría, Carlos, et al.
Published: (2022) -
Causal Inference Isn't Special: Why It's Just Another Prediction Problem
by: Fernández-Loría, Carlos
Published: (2025) -
Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs
by: Björkman, Zachris, et al.
Published: (2025) -
Local Causal Discovery for Estimating Causal Effects
by: Gupta, Shantanu, et al.
Published: (2023) -
Honesty in Causal Forests: When It Helps and When It Hurts
by: Hou, Yanfang, et al.
Published: (2025)