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Adaptive Split Balancing for Optimal Random Forest
While random forests are commonly used for regression problems, existing methods often lack adaptability in complex situations or lose optimality under simple, smooth scenarios.
Dynamic treatment effects: high-dimensional inference under model misspecification
This paper introduces a new approach by proposing novel, robust estimators for both treatment assignments and outcome models.
High-dimensional Inference for Dynamic Treatment Effects
Estimating dynamic treatment effects is a crucial endeavor in causal inference, particularly when confronted with high-dimensional confounders.
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