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Publications
  • 2023
  • Article
  • Journal of Machine Learning Research

Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators

By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
  • Format:Electronic
  • | Pages:57
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Abstract

Regression discontinuity (RD) designs are widely used to estimate causal effects in the absence of a randomized experiment. However, standard approaches to RD analysis face two significant limitations. First, they require a priori knowledge of discontinuities in treatment. Second, they yield doubly-local treatment effect estimates, and fail to provide more general causal effect estimates away from the discontinuity. To address these limitations, we introduce a novel method for automatically detecting RDs at scale, integrating information from multiple discovered discontinuities with an observational estimator, and extrapolating away from discovered, local RDs. We demonstrate the performance of our method on two synthetic datasets, showing improved performance compared to direct use of an observational estimator, direct extrapolation of RD estimates, and existing methods for combining multiple causal effect estimates. Finally, we apply our novel method to estimate spatially heterogeneous treatment effects in the context of a recent economic development problem.

Keywords

Regression Discontinuity Design; Analytics and Data Science; AI and Machine Learning

Citation

Jakubowski, Benjamin, Siram Somanchi, Edward McFowland III, and Daniel B. Neill. "Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators." Journal of Machine Learning Research 24, no. 133 (2023): 1–57.
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About The Author

Edward McFowland III

Technology and Operations Management
→More Publications

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    Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality

    By: Frabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani
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    So, Who Likes You? Evidence from a Randomized Field Experiment

    By: Ravi Bapna, Edward McFowland III, Probal Mojumder, Jui Ramaprasad and Akhmed Umyarov
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    Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness

    By: Neil Menghani, Edward McFowland III and Daniel B. Neill
More from the Authors
  • Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality By: Frabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani
  • So, Who Likes You? Evidence from a Randomized Field Experiment By: Ravi Bapna, Edward McFowland III, Probal Mojumder, Jui Ramaprasad and Akhmed Umyarov
  • Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness By: Neil Menghani, Edward McFowland III and Daniel B. Neill
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