Hierarchical and Density-based Causal Clustering
Fuente:
arXiv
Saved in:
| Main Authors: | Kim, Kwangho, Kim, Jisu, Wasserman, Larry A., Kennedy, Edward H. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Causal K-Means Clustering
by: Kim, Kwangho, et al.
Published: (2024)
by: Kim, Kwangho, et al.
Published: (2024)
Causal effects based on distributional distances
by: Kim, Kwangho, et al.
Published: (2018)
by: Kim, Kwangho, et al.
Published: (2018)
Topological Causal Effects
by: Kim, Kwangho, et al.
Published: (2026)
by: Kim, Kwangho, et al.
Published: (2026)
Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
by: Kim, Kwangho
Published: (2026)
by: Kim, Kwangho
Published: (2026)
Semiparametric Counterfactual Regression
by: Kim, Kwangho
Published: (2025)
by: Kim, Kwangho
Published: (2025)
Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
by: Du, Jin-Hong, et al.
Published: (2024)
by: Du, Jin-Hong, et al.
Published: (2024)
The Fundamental Limits of Structure-Agnostic Functional Estimation
by: Balakrishnan, Sivaraman, et al.
Published: (2023)
by: Balakrishnan, Sivaraman, et al.
Published: (2023)
Double Cross-fit Doubly Robust Estimators: Beyond Series Regression
by: McClean, Alec, et al.
Published: (2024)
by: McClean, Alec, et al.
Published: (2024)
Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression
by: Kim, Kwangho, et al.
Published: (2026)
by: Kim, Kwangho, et al.
Published: (2026)
Semi-Supervised U-statistics
by: Kim, Ilmun, et al.
Published: (2024)
by: Kim, Ilmun, et al.
Published: (2024)
Causal Effect Estimation after Propensity Score Trimming with Continuous Treatments
by: Branson, Zach, et al.
Published: (2023)
by: Branson, Zach, et al.
Published: (2023)
Simultaneous inference for generalized linear models with unmeasured confounders
by: Du, Jin-Hong, et al.
Published: (2023)
by: Du, Jin-Hong, et al.
Published: (2023)
Hierarchical Causal Models
by: Weinstein, Eli N., et al.
Published: (2024)
by: Weinstein, Eli N., et al.
Published: (2024)
Assumption-Lean Post-Integrated Inference with Surrogate Control Outcomes
by: Du, Jin-Hong, et al.
Published: (2024)
by: Du, Jin-Hong, et al.
Published: (2024)
Statistical Properties of Rectified Flow
by: Mena, Gonzalo, et al.
Published: (2025)
by: Mena, Gonzalo, et al.
Published: (2025)
Disentangled Feature Importance
by: Du, Jin-Hong, et al.
Published: (2025)
by: Du, Jin-Hong, et al.
Published: (2025)
Nonlinear Regression with Residuals: Causal Estimation with Time-varying Treatments and Covariates
by: Bates, Stephen, et al.
Published: (2022)
by: Bates, Stephen, et al.
Published: (2022)
Universal Inference Meets Random Projections: A Scalable Test for Log-concavity
by: Dunn, Robin, et al.
Published: (2021)
by: Dunn, Robin, et al.
Published: (2021)
Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectives
by: Balakrishnan, Sivaraman, et al.
Published: (2025)
by: Balakrishnan, Sivaraman, et al.
Published: (2025)
Clustering and Pruning in Causal Data Fusion
by: Tabell, Otto, et al.
Published: (2025)
by: Tabell, Otto, et al.
Published: (2025)
Towards Identifiability of Hierarchical Temporal Causal Representation Learning
by: Li, Zijian, et al.
Published: (2025)
by: Li, Zijian, et al.
Published: (2025)
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)
Agglomerative Hierarchical Clustering for Selecting Valid Instrumental Variables
by: Apfel, Nicolas, et al.
Published: (2021)
by: Apfel, Nicolas, et al.
Published: (2021)
Hierarchical Linkage Clustering Beyond Binary Trees and Ultrametrics
by: Dreveton, Maximilien, et al.
Published: (2025)
by: Dreveton, Maximilien, et al.
Published: (2025)
Controllable Generative Sandbox for Causal Inference
by: Zhang, Qi, et al.
Published: (2026)
by: Zhang, Qi, et al.
Published: (2026)
Hierarchical Clustering With Confidence
by: Wu, Di, et al.
Published: (2025)
by: Wu, Di, et al.
Published: (2025)
HOLOGRAPH: Active Causal Discovery via Sheaf-Theoretic Alignment of Large Language Model Priors
by: Kim, Hyunjun
Published: (2025)
by: Kim, Hyunjun
Published: (2025)
A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
by: Du, Honglin, et al.
Published: (2026)
by: Du, Honglin, et al.
Published: (2026)
Estimation and Inference for Causal Functions with Multiway Clustered Data
by: Liu, Nan, et al.
Published: (2024)
by: Liu, Nan, et al.
Published: (2024)
GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Imaging Studies
by: Nguyen, Minh, et al.
Published: (2022)
by: Nguyen, Minh, et al.
Published: (2022)
Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning
by: Fan, Di, et al.
Published: (2023)
by: Fan, Di, et al.
Published: (2023)
Continuous Treatment Effects with Surrogate Outcomes
by: Zeng, Zhenghao, et al.
Published: (2024)
by: Zeng, Zhenghao, et al.
Published: (2024)
Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
by: Park, Soojin, et al.
Published: (2025)
by: Park, Soojin, et al.
Published: (2025)
Causal Geodesy: Counterfactual Estimation Along the Path Between Correlation and Causation
by: Schindl, Kyle, et al.
Published: (2025)
by: Schindl, Kyle, et al.
Published: (2025)
Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
by: Rambachan, Ashesh, et al.
Published: (2022)
by: Rambachan, Ashesh, et al.
Published: (2022)
Structural Causality-based Generalizable Concept Discovery Models
by: Sinha, Sanchit, et al.
Published: (2024)
by: Sinha, Sanchit, et al.
Published: (2024)
Optimization-based Causal Estimation from Heterogenous Environments
by: Yin, Mingzhang, et al.
Published: (2021)
by: Yin, Mingzhang, et al.
Published: (2021)
On the Role of Surrogates in Conformal Inference of Individual Causal Effects
by: Gao, Chenyin, et al.
Published: (2024)
by: Gao, Chenyin, et al.
Published: (2024)
Optimal Kernel Choice for Score Function-based Causal Discovery
by: Wang, Wenjie, et al.
Published: (2024)
by: Wang, Wenjie, et al.
Published: (2024)
Causal-learn: Causal Discovery in Python
by: Zheng, Yujia, et al.
Published: (2023)
by: Zheng, Yujia, et al.
Published: (2023)
Similar Items
-
Causal K-Means Clustering
by: Kim, Kwangho, et al.
Published: (2024) -
Causal effects based on distributional distances
by: Kim, Kwangho, et al.
Published: (2018) -
Topological Causal Effects
by: Kim, Kwangho, et al.
Published: (2026) -
Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
by: Kim, Kwangho
Published: (2026) -
Semiparametric Counterfactual Regression
by: Kim, Kwangho
Published: (2025)