PLRD: Partially Linear Regression Discontinuity Inference -- by Aditya Ghosh, Guido Imbens, Stefan Wager

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2026, 18th Annual Feldstein Lecture, Mark Duggan, "Social Security Reform in an Era of Fiscal Deficits" Primary tabs
  • Acknowledgements and Disclosures

    We are grateful to Evan Munro for sharing results on the behavior of existing methods for RDDs under generative adversarial simulation following (Athey et al., 2024). We are also grateful to Tim Armstrong, P. Aronow, Max Farrell, Paul Goldsmith-Pinkham, Calumn Hamilton, Michal Kolesár, Christoph Rothe and Rocío Titiunik for insightful conversations and for comments on a previous version of this paper. This work was supported by the National Science Foundation under grant SES-2242876, the Office of Naval Research under grants N00014-17-1-2131 and N00014-19-1-2468, and a gift from Amazon. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.


    Guido Imbens

    I have consulted for Facebook, Amazon, Keystone Corporation, and Lilly Corporation.

    I have consulted for Facebook, Keystone, Lily Corporation and Amazon.

  • Citation and Citation DataCopy Citation Aditya Ghosh, Guido Imbens, and Stefan Wager, "PLRD: Partially Linear Regression Discontinuity Inference," NBER Working Paper 35699 (2026), https://doi.org/10.3386/w35699. Copy to Clipboard Download Citation
SI 2026 Methods Lecture
Using AI in Household Finance Research: A Practical Guide

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本文由NBER于2026年9月3日首次发布。HUE Legacy Ventures 并非本文作者。

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