Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning
Fuente:
arXiv
Guardado en:
| Autor principal: | Tanaka, Masahiro |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference
por: Kosko, Matthew, et al.
Publicado: (2026)
por: Kosko, Matthew, et al.
Publicado: (2026)
Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
por: Kato, Masahiro
Publicado: (2025)
por: Kato, Masahiro
Publicado: (2025)
Causal Effect Estimation with Learned Instrument Representations
por: Dean, Frances, et al.
Publicado: (2026)
por: Dean, Frances, et al.
Publicado: (2026)
Local Causal Discovery for Estimating Causal Effects
por: Gupta, Shantanu, et al.
Publicado: (2023)
por: Gupta, Shantanu, et al.
Publicado: (2023)
Transfer Learning for Causal Effect Estimation
por: Wei, Song, et al.
Publicado: (2023)
por: Wei, Song, et al.
Publicado: (2023)
Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap
por: Clivio, Oscar, et al.
Publicado: (2026)
por: Clivio, Oscar, et al.
Publicado: (2026)
The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects
por: Fernández-Loría, Carlos, et al.
Publicado: (2025)
por: Fernández-Loría, Carlos, et al.
Publicado: (2025)
Causal-Policy Forest for End-to-End Policy Learning
por: Kato, Masahiro
Publicado: (2025)
por: Kato, Masahiro
Publicado: (2025)
Direct Bias-Correction Term Estimation for Average Treatment Effect Estimation
por: Kato, Masahiro
Publicado: (2025)
por: Kato, Masahiro
Publicado: (2025)
An Overview of Causal Inference using Kernel Embeddings
por: Sejdinovic, Dino
Publicado: (2024)
por: Sejdinovic, Dino
Publicado: (2024)
A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
por: Wu, Ying, et al.
Publicado: (2023)
por: Wu, Ying, et al.
Publicado: (2023)
Bridging the Gap between Empirical Welfare Maximization and Conditional Average Treatment Effect Estimation in Policy Learning
por: Kato, Masahiro
Publicado: (2025)
por: Kato, Masahiro
Publicado: (2025)
Considerations for Estimating Causal Effects of Informatively Timed Treatments
por: Oganisian, Arman
Publicado: (2025)
por: Oganisian, Arman
Publicado: (2025)
The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation
por: Machlanski, Damian, et al.
Publicado: (2023)
por: Machlanski, Damian, et al.
Publicado: (2023)
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
por: Fuhr, Jonathan, et al.
Publicado: (2024)
por: Fuhr, Jonathan, et al.
Publicado: (2024)
A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation
por: Shi, Jieru, et al.
Publicado: (2023)
por: Shi, Jieru, et al.
Publicado: (2023)
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
por: Ter-Minassian, Lucile, et al.
Publicado: (2024)
por: Ter-Minassian, Lucile, et al.
Publicado: (2024)
Optimal Kernel Choice for Score Function-based Causal Discovery
por: Wang, Wenjie, et al.
Publicado: (2024)
por: Wang, Wenjie, et al.
Publicado: (2024)
A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
por: Singh, Aman
Publicado: (2026)
por: Singh, Aman
Publicado: (2026)
Detecting Changes in Causal Dependence with Kernels and Copulas
por: Gavioli-Akilagun, Shakeel, et al.
Publicado: (2026)
por: Gavioli-Akilagun, Shakeel, et al.
Publicado: (2026)
Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding
por: Hafer, Marlies, et al.
Publicado: (2025)
por: Hafer, Marlies, et al.
Publicado: (2025)
A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects
por: Pham, Khiem, et al.
Publicado: (2023)
por: Pham, Khiem, et al.
Publicado: (2023)
RepFlow: Representation Enhanced Flow Matching for Causal Effect Estimation
por: Xie, Yifei, et al.
Publicado: (2026)
por: Xie, Yifei, et al.
Publicado: (2026)
A Unified Theory for Causal Inference: Direct Debiased Machine Learning via Bregman-Riesz Regression
por: Kato, Masahiro
Publicado: (2025)
por: Kato, Masahiro
Publicado: (2025)
Compositional Models for Estimating Causal Effects
por: Pruthi, Purva, et al.
Publicado: (2024)
por: Pruthi, Purva, et al.
Publicado: (2024)
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
por: Dong, Shuyu, et al.
Publicado: (2024)
por: Dong, Shuyu, et al.
Publicado: (2024)
Adaptive Kernel Density Estimation with Pre-training
por: Zhang, Ruitong, et al.
Publicado: (2026)
por: Zhang, Ruitong, et al.
Publicado: (2026)
Recursive Estimation of Conditional Kernel Mean Embeddings
por: Tamás, Ambrus, et al.
Publicado: (2023)
por: Tamás, Ambrus, et al.
Publicado: (2023)
Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices
por: Kato, Masahiro, et al.
Publicado: (2024)
por: Kato, Masahiro, et al.
Publicado: (2024)
Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivity
por: Wiederkehr, Christoph, et al.
Publicado: (2025)
por: Wiederkehr, Christoph, et al.
Publicado: (2025)
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment
por: Hu, Robert, et al.
Publicado: (2021)
por: Hu, Robert, et al.
Publicado: (2021)
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
por: Zhu, Hongtao, et al.
Publicado: (2024)
por: Zhu, Hongtao, et al.
Publicado: (2024)
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
por: Dhir, Anish, et al.
Publicado: (2025)
por: Dhir, Anish, et al.
Publicado: (2025)
Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation
por: Pros, Roger, et al.
Publicado: (2024)
por: Pros, Roger, et al.
Publicado: (2024)
ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation
por: Kato, Masahiro
Publicado: (2025)
por: Kato, Masahiro
Publicado: (2025)
Causal Inference on Stopped Random Walks in Online Advertising
por: Yu, Jia Yuan
Publicado: (2026)
por: Yu, Jia Yuan
Publicado: (2026)
Undersmoothing Causal Estimators with Generative Trees
por: Machlanski, Damian, et al.
Publicado: (2022)
por: Machlanski, Damian, et al.
Publicado: (2022)
CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect
por: Zhou, Jiehui, et al.
Publicado: (2024)
por: Zhou, Jiehui, et al.
Publicado: (2024)
Topological Causal Effects
por: Kim, Kwangho, et al.
Publicado: (2026)
por: Kim, Kwangho, et al.
Publicado: (2026)
Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects
por: Kato, Masahiro
Publicado: (2024)
por: Kato, Masahiro
Publicado: (2024)
Ejemplares similares
-
Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference
por: Kosko, Matthew, et al.
Publicado: (2026) -
Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
por: Kato, Masahiro
Publicado: (2025) -
Causal Effect Estimation with Learned Instrument Representations
por: Dean, Frances, et al.
Publicado: (2026) -
Local Causal Discovery for Estimating Causal Effects
por: Gupta, Shantanu, et al.
Publicado: (2023) -
Transfer Learning for Causal Effect Estimation
por: Wei, Song, et al.
Publicado: (2023)