Cover Image

Deep Learning for Individual Heterogeneity

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Farrell, Max H., Liang, Tengyuan, Misra, Sanjog
Format:	Preprint
Published:	2020
Subjects:	Econometrics Machine Learning Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Foundation Priors
by: Misra, Sanjog
Published: (2025)

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

Regularized DeepIV with Model Selection
by: Li, Zihao, et al.
Published: (2024)

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

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

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

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

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

General Bayesian Policy Learning
by: Kato, Masahiro
Published: (2026)

Policy Learning with Distributional Welfare
by: Cui, Yifan, et al.
Published: (2023)

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

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

Ordinary Least Squares as an Attention Mechanism
by: Coulombe, Philippe Goulet
Published: (2025)

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

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

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

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

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

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

Nested Nonparametric Instrumental Variable Regression
by: Meza, Isaac, et al.
Published: (2021)

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

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

Decomposition of Spillover Effects Under Misspecification: Pseudo-true Estimands and a Local-Global Extension
by: Park, Yechan, et al.
Published: (2026)

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

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

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

Identification and Inference in Nonlinear Dynamic Network Models
by: Vallarino, Diego
Published: (2026)

Kernel methods for long term dose response curves
by: Singh, Rahul, et al.
Published: (2022)

Causal-Policy Forest for End-to-End Policy Learning
by: Kato, Masahiro
Published: (2025)

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

Direct Debiased Machine Learning via Bregman Divergence Minimization
by: Kato, Masahiro
Published: (2025)

Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data
by: Sakaguchi, Shosei
Published: (2024)

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

Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters
by: Dalal, Abhinandan, et al.
Published: (2024)

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

genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
by: Kato, Masahiro
Published: (2026)

ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation
by: Kato, Masahiro
Published: (2025)

Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
by: Kato, Masahiro
Published: (2026)

A Unified Framework for Debiased Machine Learning: Riesz Representer Fitting under Bregman Divergence
by: Kato, Masahiro
Published: (2026)

A Unified Theory for Causal Inference: Direct Debiased Machine Learning via Bregman-Riesz Regression
by: Kato, Masahiro
Published: (2025)