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Estimating Dyadic Treatment Effects with Unknown Confounders

Tadao Hoshino, Takahide Yanagi

arXiv 26 May 2024 · Econometrics

arXiv:2405.16547 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper proposes a statistical inference method for assessing treatment effects with dyadic data. Under the assumption that the treatments follow an exchangeable distribution, our approach allows for the presence of any unobserved confounding factors that potentially cause endogeneity of treatment choice without requiring additional information other than the treatments and outcomes. Building on the literature of graphon estimation in network data analysis, we propose a neighborhood kernel smoothing method for estimating dyadic average treatment effects. We also develop a permutation inference method for testing the sharp null hypothesis. Under certain regularity conditions, we derive the rate of convergence of the proposed estimator and demonstrate the size control property of our test. We apply our method to international trade data to assess the impact of free trade agreements on bilateral trade flows.

Citation extraction

34
references
57
in-text mentions
34
distinct cited
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self-citations
8,975
main-text words

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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
1Zhang, Y., Levina, E., and Zhu, J (2017) Estimating network edge probabilities by neighbourhood smoothing0.91613377%
2Klopp, O., Tsybakov, A.B., and Verzelen, N (2017) Oracle inequalities for network models and sparse graphon estimation0.84333100%
3Gao, C., Lu, Y., and Zhou, H.H (2015) Rate-optimal graphon estimation0.73732100%
4Nagengast, A. and Yotov, Y.V (2023) Staggered difference-in-differences in gravity settings: Revisiting the effects of trade agreements0.73732100%
5Chan, S. and Airoldi, E (2014) A consistent histogram estimator for exchangeable graph models0.64422100%
6Graham, B.S (2017) An econometric model of network formation with degree heterogeneity0.64422100%
7Tinbergen, J (1962) Shaping the world economy; suggestions for an international economic policy0.64422100%
8Arpino, B., De Benedictis, L., and Mattei, A (2017) Implementing propensity score matching with network data: the effect of the general agreement on tariffs and trade on bilateral…0.51121100%
9Baier, S.L. and Bergstrand, J.H (2007) Do free trade agreements actually increase members' international trade?0.51121100%
10Airoldi, E.M., Blei, D., Fienberg, S., and Xing, E (2008) Mixed membership stochastic blockmodels0.40511100%

Showing the top 10 of 34 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
1Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities0.51121