A two-stage kernel ridge regression estimator for continuous treatment effects adapts automatically to unknown overlap and kernel regularity via data-driven selection.
Obtain the final estimator ˆηλ = (A⊤A+nλI) −1 nX j=1 ϕ(a′ j)m(a′ j) = (A⊤A+nλI) −1A⊤Wˆθ
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Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression
A two-stage kernel ridge regression estimator for continuous treatment effects adapts automatically to unknown overlap and kernel regularity via data-driven selection.