Generalized Riesz Regression: A Unified Framework for Debiased Machine Learning with Riesz Representer Fitting under Bregman Divergence
econ.EM, cs.LG, math.ST, stat.ME, stat.ML, stat.TH
Submitted: 2026-01-12
Updated: 2026-08-25
Code: https://github.com/MasaKat0/genriesz
Project page: https://alejandroschuler.github.io/mci
Terminology
Sources
- Direct Bias-Correction Term Estimation for Average Treatment Effect Estimation
- A Unified Theory for Causal Inference: Direct Debiased Machine Learning via Bregman-Riesz Regression
- Direct Debiased Machine Learning via Bregman Divergence Minimization
- The Balancing Act in Causal Inference
- Automatic Debiased Machine Learning via Riesz Regression
- Automatic Debiased Machine Learning for Covariate Shifts
- Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models
- Nearest Neighbor Matching as Least Squares Density Ratio Estimation and Riesz Regression
- Riesz Regression As Direct Density Ratio Estimation
- ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation
- Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
- Double Debiased Covariate Shift Adaptation Robust to Density-Ratio Estimation
- PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
- RieszBoost: Gradient Boosting for Riesz Regression
- Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiency
- General Bayesian inference for causal effects using covariate balancing procedure
- Distributional Balancing for Causal Inference: A Unified Framework via Characteristic Function Distance
- Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension
- Generative Modeling by Estimating Gradients of the Data Distribution
- Likelihood-free inference by ratio estimation
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