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Statistical Treatment Rules under Social Interaction

Seungjin Han, Julius Owusu, Youngki Shin

arXiv 19 Sep 2022 · Econometrics

arXiv:2209.09077 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In this paper we study treatment assignment rules in the presence of social interaction. We construct an analytical framework under the anonymous interaction assumption, where the decision problem becomes choosing a treatment fraction. We propose a multinomial empirical success (MES) rule that includes the empirical success rule of Manski (2004) as a special case. We investigate the non-asymptotic bounds of the expected utility based on the MES rule. Finally, we prove that the MES rule achieves the asymptotic optimality with the minimax regret criterion.

Citation extraction

29
references
64
in-text mentions
29
distinct cited
0
self-citations
10,495
main-text words

appendix boundary found by appendix_command · 70% of the source is main text. Read the extracted text to check this.

Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Manski, C. F (2004) Statistical treatment rules for heterogeneous populations1.000144100%
2Stoye, J (2009) Minimax regret treatment choice with finite samples1.00063100%
3Hirano, K. and J. R. Porter (2009) Asymptotics for statistical treatment rules0.94613485%
4Manski, C. F (2013) Identification of treatment response with social interactions0.73732100%
5Manski, C. F. and A. Tetenov (2016) Sufficient trial size to inform clinical practice0.64422100%
6Baird, S., J. A. Bohren, C. McIntosh, and B. Özler (2018) Optimal design of experiments in the presence of interference0.51121100%
7Van der Vaart, A (1991) An asymptotic representation theorem0.51121100%
8Athey, S. and S. Wager (2021) Policy learning with observational data0.40511100%
9Beaman, L. A (2012) Social networks and the dynamics of labour market outcomes: Evidence from refugees resettled in the us0.40511100%
10Bickel, P. J., C. A. Klaassen, P. J. Bickel, Y. Ritov, J. Klaassen,… (1993) Efficient and adaptive estimation for semiparametric models, Volume 40.40511100%

Showing the top 10 of 29 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1A Nonparametric Test of Heterogeneous Treatment Effects under Interference0.40511
2Randomization Inference of Heterogeneous Treatment Effects under Network Interference0.00011