Quasi-randomization tests for network interference

Supriya Tiwari and Pallavi Basu

Papers from arXiv.org

Abstract: Network interference amounts to the treatment status of one unit affecting the potential outcome of other units in the population. Testing for spillover effects in this setting makes the null hypothesis non-sharp. An interesting approach to tackling the non-sharp nature of the null hypothesis in this setup is constructing conditional randomization tests such that the null is sharp on the restricted population. Such approaches can pose computational challenges as finding these appropriate sub-populations based on experimental design can involve solving an NP-hard problem. In this paper, we view the network amongst the population as a random variable instead of being fixed. We propose a new approach that builds a conditional quasi-randomization test. We build the (non-sharp) null distribution of no spillover effects using random graph null models. We show that our method is exactly valid in finite samples under mild assumptions. Our method displays enhanced power over other methods, substantially improving cluster randomized trials. We illustrate our methodology to test for interference in a weather insurance adoption experiment run in rural China.

Date: 2024-03, Revised 2024-10
New Economics Papers: this item is included in nep-ecm, nep-exp and nep-net
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2403.16673 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2403.16673

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().