Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes
6 Pith papers cite this work. Polarity classification is still indexing.
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NBPL uses a nonparametric Dirichlet process prior on the reduced-form distribution for posterior inference on optimal treatment assignments and welfare, with minimax-optimal regret convergence and pointwise consistent policy class comparisons.
Causal stability selection identifies treatment effect modifiers with a non-asymptotic bound on expected false positives by integrating cross-fitted CATE estimation and stability selection.
Conformal inference produces robust prediction intervals for treatment effects under experimental attrition, outperforming complete-case, imputation, and weighting approaches in simulations.
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.
citing papers explorer
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The Statistical Cost of Adaptation in Multi-Source Transfer Learning
Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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Nonparametric Bayesian Policy Learning
NBPL uses a nonparametric Dirichlet process prior on the reduced-form distribution for posterior inference on optimal treatment assignments and welfare, with minimax-optimal regret convergence and pointwise consistent policy class comparisons.
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Causal Stability Selection
Causal stability selection identifies treatment effect modifiers with a non-asymptotic bound on expected false positives by integrating cross-fitted CATE estimation and stability selection.
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Conformal Inference for Experimental Attrition in Social Science Research
Conformal inference produces robust prediction intervals for treatment effects under experimental attrition, outperforming complete-case, imputation, and weighting approaches in simulations.
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Assessing Estimate of CATE from Observational Data via an RCT Study
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
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A Review of Causal Decision Making
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.