arXiv 26 Apr 2021 · Econometrics · 2 citations (OpenAlex)
arXiv:2104.12909 · PDF · DOI · OpenAlex · Extracted main text
Algorithms make a growing portion of policy and business decisions. We develop a treatment-effect estimator using algorithmic decisions as instruments for a class of stochastic and deterministic algorithms. Our estimator is consistent and asymptotically normal for well-defined causal effects. A special case of our setup is multidimensional regression discontinuity designs with complex boundaries. We apply our estimator to evaluate the Coronavirus Aid, Relief, and Economic Security Act, which allocated many billions of dollars worth of relief funding to hospitals via an algorithmic rule. The funding is shown to have little effect on COVID-19-related hospital activities. Naive estimates exhibit selection bias.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Zajonc, T (2012) Regression Discontinuity Design with Multiple Forcing Variables | 0.920 | 9 | 3 | 78% |
| 2 | Keele, L. J. and Titiunik, R (2015) Geographic Boundaries as Regression Discontinuities | 0.874 | 6 | 4 | 67% |
| 3 | Hahn, J., Todd, P. and van der Klaauw, W (2001) Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design | 0.811 | 4 | 2 | 100% |
| 4 | –- and Wager, S (2019) Optimized Regression Discontinuity Designs | 0.737 | 5 | 3 | 40% |
| 5 | –- (2021) b) | 0.737 | 3 | 3 | 67% |
| 6 | –-, –-, –- and Pathak, P. A (2022) Breaking Ties: Regression Discontinuity Design Meets Market Design | 0.644 | 3 | 2 | 67% |
| 7 | Kakani, P., Chandra, A., Mullainathan, S. and Obermeyer, Z (2020) Allocation of COVID-19 Relief Funding to Disproportionately Black Counties | 0.644 | 3 | 2 | 67% |
| 8 | –- and Kolesár, M (2021) Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness | 0.644 | 2 | 2 | 100% |
| 9 | Imbens, G. W. and Angrist, J. D (1994) Identification and Estimation of Local Average Treatment Effects | 0.644 | 2 | 2 | 100% |
| 10 | Abdulkadiroglu, A., Angrist, J. D., Narita, Y. and Pathak, P. A (2017) Research Design Meets Market Design: Using Centralized Assignment for Impact Evaluation self | 0.511 | 2 | 2 | 50% |
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