Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing

arXiv:2603.16829 · stat.ML, cs.LG, math.ST, stat.ME, stat.TH · Submitted 2026-03-17 · Read on arXiv

stat.ML, cs.LG, math.ST, stat.ME, stat.TH

Submitted: 2026-03-17

Updated: 2026-09-22

Comments: Camera-ready version for ICML 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embeddings — 20× in this paper · explained in 2 episodes
• Regularization — 11× in this paper · explained in 3 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Hilbert Space — 7× in this paper · explained in 2 episodes
• Distribution Shift — 6× in this paper · explained in 5 episodes
• Minimax optimality — 5× in this paper · explained in 2 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Feature Extraction — 4× in this paper · explained in 2 episodes
• Homogeneity — 4× in this paper · explained in 2 episodes
• Causal Inference — 3× in this paper · explained in 4 episodes
• Score Function — 3× in this paper · explained in 4 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Total Wealth — 2× in this paper
• Action Space — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Agnostic Learning — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Monte Carlo — 1× in this paper
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Minimax Optimal Kernel Two-Sample Tests with Random Features
• Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest
• Neural-Kernel Conditional Mean Embeddings

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