Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
Fuente:
arXiv
Saved in:
| Main Authors: | Ter-Minassian, Lucile, Szlak, Liran, Karavani, Ehud, Holmes, Chris, Shimoni, Yishai |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Propensity score models are better when post-calibrated
by: Gutman, Rom, et al.
Published: (2022)
by: Gutman, Rom, et al.
Published: (2022)
Explainable AI for survival analysis: a median-SHAP approach
by: Ter-Minassian, Lucile, et al.
Published: (2024)
by: Ter-Minassian, Lucile, et al.
Published: (2024)
Democratizing AI Governance: Balancing Expertise and Public Participation
by: Ter-Minassian, Lucile
Published: (2025)
by: Ter-Minassian, Lucile
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)
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition
by: Fawkes, Jake, et al.
Published: (2024)
by: Fawkes, Jake, et al.
Published: (2024)
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference
by: Clivio, Oscar, et al.
Published: (2024)
by: Clivio, Oscar, et al.
Published: (2024)
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)
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)
Local Causal Discovery for Estimating Causal Effects
by: Gupta, Shantanu, et al.
Published: (2023)
by: Gupta, Shantanu, et al.
Published: (2023)
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)
Hierarchical Causal Models
by: Weinstein, Eli N., et al.
Published: (2024)
by: Weinstein, Eli N., et al.
Published: (2024)
Using LLMs to Directly Guess Conditional Expectations Can Improve Efficiency in Causal Estimation
by: Engh, Chris, et al.
Published: (2025)
by: Engh, Chris, 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)
Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
by: Bargagli-Stoffi, Falco J., et al.
Published: (2020)
by: Bargagli-Stoffi, Falco J., et al.
Published: (2020)
Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors
by: Dhawan, Nikita, et al.
Published: (2026)
by: Dhawan, Nikita, et al.
Published: (2026)
CausalFormer: An Interpretable Transformer for Temporal Causal Discovery
by: Kong, Lingbai, et al.
Published: (2024)
by: Kong, Lingbai, et al.
Published: (2024)
The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation
by: Machlanski, Damian, et al.
Published: (2023)
by: Machlanski, Damian, et al.
Published: (2023)
Considerations for Estimating Causal Effects of Informatively Timed Treatments
by: Oganisian, Arman
Published: (2025)
by: Oganisian, Arman
Published: (2025)
End-To-End Causal Effect Estimation from Unstructured Natural Language Data
by: Dhawan, Nikita, et al.
Published: (2024)
by: Dhawan, Nikita, et al.
Published: (2024)
Hierarchical and Density-based Causal Clustering
by: Kim, Kwangho, et al.
Published: (2024)
by: Kim, Kwangho, et al.
Published: (2024)
Coarsening Bias from Variable Discretization in Causal Functionals
by: Ou, Xiaxian, et al.
Published: (2026)
by: Ou, Xiaxian, et al.
Published: (2026)
Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning
by: Tanaka, Masahiro
Published: (2025)
by: Tanaka, Masahiro
Published: (2025)
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)
Lost in Aggregation: The Causal Interpretation of the IV Estimand
by: Tsao, Danielle, et al.
Published: (2026)
by: Tsao, Danielle, et al.
Published: (2026)
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)
Bias-Targeted Nonparametric Balancing for Stable Causal Mediation Analysis
by: Liu, Chang, et al.
Published: (2024)
by: Liu, Chang, et al.
Published: (2024)
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)
Towards a holistic understanding of Selection Bias for Causal Effect Identification
by: Qiu, Yiwen, et al.
Published: (2026)
by: Qiu, Yiwen, et al.
Published: (2026)
Towards Identifiability of Hierarchical Temporal Causal Representation Learning
by: Li, Zijian, et al.
Published: (2025)
by: Li, Zijian, et al.
Published: (2025)
Linking Model Intervention to Causal Interpretation in Model Explanation
by: Cheng, Debo, et al.
Published: (2024)
by: Cheng, Debo, et al.
Published: (2024)
Targeting relative risk heterogeneity with causal forests
by: Shirvaikar, Vik, et al.
Published: (2023)
by: Shirvaikar, Vik, et al.
Published: (2023)
Local Interference: Removing Interference Bias in Semi-Parametric Causal Models
by: O'Riordan, Michael, et al.
Published: (2025)
by: O'Riordan, Michael, et al.
Published: (2025)
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)
Bias and Uncertainty in LLM-as-a-Judge Estimation
by: Fiedler, James
Published: (2026)
by: Fiedler, James
Published: (2026)
Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
by: Waxman, Daniel, et al.
Published: (2024)
by: Waxman, Daniel, et al.
Published: (2024)
Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms
by: Hong, Sungchul, et al.
Published: (2023)
by: Hong, Sungchul, et al.
Published: (2023)
Undersmoothing Causal Estimators with Generative Trees
by: Machlanski, Damian, et al.
Published: (2022)
by: Machlanski, Damian, et al.
Published: (2022)
Topological Causal Effects
by: Kim, Kwangho, et al.
Published: (2026)
by: Kim, Kwangho, et al.
Published: (2026)
Similar Items
-
Propensity score models are better when post-calibrated
by: Gutman, Rom, et al.
Published: (2022) -
Explainable AI for survival analysis: a median-SHAP approach
by: Ter-Minassian, Lucile, et al.
Published: (2024) -
Democratizing AI Governance: Balancing Expertise and Public Participation
by: Ter-Minassian, Lucile
Published: (2025) -
Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap
by: Clivio, Oscar, et al.
Published: (2026) -
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition
by: Fawkes, Jake, et al.
Published: (2024)