Imagen de Portada

Decomposition of Spillover Effects Under Misspecification: Pseudo-true Estimands and a Local-Global Extension

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Park, Yechan, Yang, Xiaodong
Formato:	Preprint
Publicado:	2026
Materias:	Econometrics Statistics Theory Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Distance and Kernel-Based Measures for Global and Local Two-Sample Conditional Distribution Testing
por: Yan, Jian, et al.
Publicado: (2022)

On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization
por: Byambadalai, Undral, et al.
Publicado: (2025)

Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction
por: Byambadalai, Undral, et al.
Publicado: (2024)

What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness
por: Cai, Yang, et al.
Publicado: (2025)

The Local Approach to Causal Inference under Network Interference
por: Auerbach, Eric, et al.
Publicado: (2021)

Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
por: Ghassami, AmirEmad, et al.
Publicado: (2022)

Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference
por: Casini, Alessandro, et al.
Publicado: (2021)

Locally Optimal Fixed-Budget Best Arm Identification in Two-Armed Gaussian Bandits with Unknown Variances
por: Kato, Masahiro
Publicado: (2023)

Identification and Debiased Learning of Causal Effects with General Instrumental Variables
por: Chen, Shuyuan, et al.
Publicado: (2025)

Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables
por: Chen, Shuyuan, et al.
Publicado: (2026)

Direct Bias-Correction Term Estimation for Average Treatment Effect Estimation
por: Kato, Masahiro
Publicado: (2025)

Gaussian and Bootstrap Approximation for Matching-based Average Treatment Effect Estimators
por: Shi, Zhaoyang, et al.
Publicado: (2024)

Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
por: Jin, Jikai, et al.
Publicado: (2024)

Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
por: Chihara, Naoki, et al.
Publicado: (2026)

Fused Extended Two-Way Fixed Effects for Difference-in-Differences With Staggered Adoptions
por: Faletto, Gregory
Publicado: (2023)

Bayesian Semiparametric Causal Inference: Targeted Doubly Robust Estimation of Treatment Effects
por: Sert, Gözde, et al.
Publicado: (2025)

Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
por: Kato, Masahiro
Publicado: (2025)

The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
por: Cattaneo, Matias D., et al.
Publicado: (2025)

PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
por: Kato, Masahiro, et al.
Publicado: (2025)

Bridging the Gap between Empirical Welfare Maximization and Conditional Average Treatment Effect Estimation in Policy Learning
por: Kato, Masahiro
Publicado: (2025)

Stochastic Deep Learning: A Probabilistic Framework for Modeling Uncertainty in Structured Temporal Data
por: Rice, James
Publicado: (2026)

The Condition-Number Principle for Prototype Clustering
por: Li, Romano, et al.
Publicado: (2026)

From Cross-Validation to SURE: Asymptotic Risk of Tuned Regularized Estimators
por: Adusumilli, Karun, et al.
Publicado: (2026)

Identification and Inference in Nonlinear Dynamic Network Models
por: Vallarino, Diego
Publicado: (2026)

Training and Testing with Multiple Splits: A Central Limit Theorem for Split-Sample Estimators
por: Fava, Bruno
Publicado: (2025)

Ordinary Least Squares as an Attention Mechanism
por: Coulombe, Philippe Goulet
Publicado: (2025)

Semiparametric inference for impulse response functions using double/debiased machine learning
por: Ballinari, Daniele, et al.
Publicado: (2024)

Is completeness necessary? Estimation in nonidentified linear models
por: Babii, Andrii, et al.
Publicado: (2017)

Minimax Semiparametric Learning With Approximate Sparsity
por: Bradic, Jelena, et al.
Publicado: (2019)

Order-Explicit Linearization of High-Dimensional $U$-Statistics
por: Ritzwoller, David M., et al.
Publicado: (2024)

A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
por: Fan, Qingliang, et al.
Publicado: (2022)

Multiply Robust Causal Mediation Analysis with Continuous Treatments
por: Xu, Yizhen, et al.
Publicado: (2021)

Nested Nonparametric Instrumental Variable Regression
por: Meza, Isaac, et al.
Publicado: (2021)

Canonical correlation regression with noisy data
por: Meza, Isaac, et al.
Publicado: (2025)

Learning bounds for doubly-robust covariate shift adaptation
por: Lee, Jeonghwan, et al.
Publicado: (2025)

Empirical Likelihood for Random Forests and Ensembles
por: Chiang, Harold D., et al.
Publicado: (2025)

Semiparametric Off-Policy Inference for Optimal Policy Values under Possible Non-Uniqueness
por: Wei, Haoyu
Publicado: (2025)

Regularized DeepIV with Model Selection
por: Li, Zihao, et al.
Publicado: (2024)

Functional Sequential Treatment Allocation with Covariates
por: Kock, Anders Bredahl, et al.
Publicado: (2020)

Deep Learning for Individual Heterogeneity
por: Farrell, Max H., et al.
Publicado: (2020)