Experimentation on Endogenous Graphs
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917206848176128 |
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| author | Wang, Wenshuo Bakhitov, Edvard Coey, Dominic |
| author_facet | Wang, Wenshuo Bakhitov, Edvard Coey, Dominic |
| contents | We study experimentation under endogenous network interference. Interference patterns are mediated by an endogenous graph, where edges can be formed or eliminated as a result of treatment. We show that conventional estimators are biased in these circumstances, and present a class of unbiased, consistent and asymptotically normal estimators of total treatment effects in the presence of such interference. We show via simulation that our estimator outperforms existing estimators in the literature. Our results apply both to bipartite experimentation, in which the units of analysis and measurement differ, and the standard network experimentation case, in which they are the same. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09267 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Experimentation on Endogenous Graphs Wang, Wenshuo Bakhitov, Edvard Coey, Dominic Methodology We study experimentation under endogenous network interference. Interference patterns are mediated by an endogenous graph, where edges can be formed or eliminated as a result of treatment. We show that conventional estimators are biased in these circumstances, and present a class of unbiased, consistent and asymptotically normal estimators of total treatment effects in the presence of such interference. We show via simulation that our estimator outperforms existing estimators in the literature. Our results apply both to bipartite experimentation, in which the units of analysis and measurement differ, and the standard network experimentation case, in which they are the same. |
| title | Experimentation on Endogenous Graphs |
| topic | Methodology |
| url | https://arxiv.org/abs/2410.09267 |