most citedA Fast Bootstrap Algorithm for Causal Inference with Large Data

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stat.ME2026

Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference

Matthew Kosko, Falco J, Bargagli-Stoffi +2

Kernel methods are widely used in causal inference for tasks such as treatment effect estimation, policy evaluation, and policy learning. The bootstrap is a standard tool for uncer…

stat.ME2023★ 1 cited

A Fast Bootstrap Algorithm for Causal Inference with Large Data

Matthew Kosko, Lin Wang, Michele Santacatterina

Estimating causal effects from large experimental and observational data has become increasingly prevalent in both industry and research. The bootstrap is an intuitive and powerful…