Wasserstein Causal Forests for Distribution-Valued Outcomes

arXiv:2609.35898 · stat.ML, cs.LG · Submitted 2026-09-27 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Benchmark — 58× in this paper · explained in 2 episodes
• Consistency — 20× in this paper · explained in 3 episodes
• Calibration — 17× in this paper · explained in 7 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Wasserstein Causal Forests — 5× in this paper
• Embeddings — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Distributional Random Forests — 3× in this paper
• Causal Inference — 2× in this paper · explained in 4 episodes
• Current Population Survey — 2× in this paper
• Score Function — 2× in this paper · explained in 4 episodes
• Agnostic Learning — 1× in this paper · explained in 1 episode
• Approximation Bound — 1× in this paper · explained in 1 episode
• Degeneracy — 1× in this paper · explained in 3 episodes
• Exchangeability — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Topological Conditional Average Treament Effect — 1× in this paper

Sources

• Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
• A Mathematical Framework for Topological Causal Data Analysis

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