arXiv 14 Sep 2024 · Econometrics
arXiv:2409.09243 · PDF · DOI · OpenAlex · Extracted main text
Researchers are often interested in the existence and extent of interference between units when conducting causal inference or designing policy. However, testing for interference presents significant econometric challenges, particularly due to complex clustering patterns and dependencies that can invalidate standard methods. This paper introduces the pairwise imputation-based randomization test (PIRT), a general and robust framework for assessing the existence and extent of interference in experimental settings. PIRT employs unconditional randomization testing and pairwise comparisons, enabling straightforward implementation and ensuring finite-sample validity under minimal assumptions about network structure. The method's practical value is demonstrated through an application to a large-scale policing experiment in Bogota, Colombia (Blattman et al., 2021), which evaluates the effects of hotspot policing on crime at the street segment level. The analysis reveals that increased police patrolling in hotspots significantly displaces violent crime, but not property crime. Simulations calibrated to this context further underscore the power and robustness of PIRT.
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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 | Blattman, C., D. P. Green, D. Ortega, and S. Tobón (2021) Place-Based Interventions at Scale: The Direct and Spillover Effects of Policing and City Services on Crime [Clustering as a Des… | 0.958 | 25 | 5 | 88% |
| 2 | Zhang, Y. and Q. Zhao (2023) What is a Randomization Test? | 0.950 | 7 | 4 | 86% |
| 3 | Athey, S., D. Eckles, and G. W. Imbens (2018) Exact p-Values for Network Interference | 0.909 | 8 | 5 | 75% |
| 4 | Basse, G., P. Ding, A. Feller, and P. Toulis (2024) Randomization Tests for Peer Effects in Group Formation Experiments | 0.894 | 7 | 4 | 71% |
| 5 | Guan, L (2023) A conformal test of linear models via permutation-augmented regressions | 0.874 | 7 | 2 | 100% |
| 6 | Hoshino, T. and T. Yanagi (2023) Randomization Test for the Specification of Interference Structure | 0.843 | 4 | 4 | 75% |
| 7 | Puelz, D., G. Basse, A. Feller, and P. Toulis (2021) A Graph-Theoretic Approach to Randomization Tests of Causal Effects under General Interference | 0.843 | 15 | 7 | 60% |
| 8 | Kelly, M (2021) Persistence, Randomization, and Spatial Noise, Working Papers 202124, School of Economics, University College Dublin | 0.843 | 3 | 3 | 100% |
| 9 | Imbens, G. W. and D. B. Rubin (2015) Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction | 0.811 | 4 | 2 | 100% |
| 10 | Basse, G. W., A. Feller, and P. Toulis (2019) Randomization tests of causal effects under interference | 0.811 | 4 | 2 | 100% |
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