Conditional average treatment effect estimation with marginally constrained models
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New Kernel Ridge Regression Method Enhances Transfer Learning for Treatment Effect Estimation
Researchers have developed a new method for transfer learning of conditional average treatment effect (CATE) using kernel ridge regression (KRR). This approach addresses challenges like covariate shift and limited overl…
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New method improves reliable selection of treatment effect predictions
Researchers have developed Denoised Conformal Alignment, a novel method for reliably selecting subsets of individuals for treatment based on predicted conditional average treatment effects (CATE). This approach addresse…