Simultaneous Inference for Local Structural Parameters with Random Forests

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Tác giả: David M Ritzwoller, Vasilis Syrgkanis

Ngôn ngữ: eng

Ký hiệu phân loại: 001.434 Experimental method

Thông tin xuất bản: 2024

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Bộ sưu tập: Metadata

ID: 202656

We construct simultaneous confidence intervals for solutions to conditional moment equations. The intervals are built around a class of nonparametric regression algorithms based on subsampled kernels. This class encompasses various forms of subsampled random forest regression, including Generalized Random Forests (Athey et al., 2019). Although simultaneous validity is often desirable in practice -- for example, for fine-grained characterization of treatment effect heterogeneity -- only confidence intervals that confer pointwise guarantees were previously available. Our work closes this gap. As a by-product, we obtain several new order-explicit results on the concentration and normal approximation of high-dimensional U-statistics.
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