arXiv 25 Mar 2024 · Statistics — Methodology
arXiv:2403.16673 · PDF · DOI · OpenAlex · Extracted main text
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. In randomized experiments, conditional randomized tests hold finite sample validity and are assumption-lean. In this paper, we incorporate 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. To build the (non-sharp) null distribution of no spillover effects, we use random graph null models. We show that our method is exactly valid in finite samples under mild assumptions. Our method displays enhanced power over state-of-the-art methods, with a substantial improvement in cluster randomized trials. We illustrate our methodology to test for interference in a weather insurance adoption experiment run in rural China.
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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 | Susan Athey, Dean Eckles, and Guido W Imbens (2018) Exact p-values for network interference | 1.000 | 14 | 4 | 100% |
| 2 | David Puelz, Guillaume Basse, Avi Feller, and Panos Toulis (2022) A graph-theoretic approach to randomization tests of causal effects under general interference | 1.000 | 8 | 3 | 100% |
| 3 | Jing Cai, Alain De Janvry, and Elisabeth Sadoulet (2015) Social networks and the decision to insure | 0.928 | 4 | 3 | 100% |
| 4 | Robert M Bond, Christopher J Fariss, Jason J Jones, Adam DI Kramer,… (2012) A 61-million-person experiment in social influence and political mobilization | 0.811 | 4 | 2 | 100% |
| 5 | Peter M Aronow and Cyrus Samii (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.737 | 3 | 2 | 100% |
| 6 | Guillaume W Basse, Avi Feller, and Panos Toulis (2019) Randomization tests of causal effects under interference | 0.737 | 3 | 2 | 100% |
| 7 | Charles F Manski (1993) Identification of endogenous social effects: The reflection problem | 0.644 | 2 | 2 | 100% |
| 8 | Mark EJ Newman, Steven H Strogatz, and Duncan J Watts (2001) Random graphs with arbitrary degree distributions and their applications | 0.644 | 2 | 2 | 100% |
| 9 | Mark EJ Newman, Duncan J Watts, and Steven H Strogatz (2002) Random graph models of social networks | 0.511 | 2 | 1 | 100% |
| 10 | Johan Ugander, Brian Karrer, Lars Backstrom, and Jon Kleinberg (2013) Graph cluster randomization: Network exposure to multiple universes | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 45 scored citations.