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Cited by 347Advanced Causal Inference TechniquesOptimal Experimental Design MethodsStatistical Methods in Clinical Trials

The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.

Estimating average causal effects under general interference, with application to a social network experiment

Open access
Aronow, Peter M., Samii, Cyrus·2017·The Annals of Applied Statistics
DOI 10.1214/16-aoas1005
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Abstract

This paper presents a randomization-based framework for estimating causal effects under interference between units motivated by challenges that arise in analyzing experiments on social networks. The framework integrates three components: (i) an experimental design that defines the probability distribution of treatment assignments, (ii) a mapping that relates experimental treatment assignments to exposures received by units in the experiment, and (iii) estimands that make use of the experiment to answer questions of substantive interest. We develop the case of estimating average unit-level causal effects from a randomized experiment with interference of arbitrary but known form. The resulting estimators are based on inverse probability weighting. We provide randomization-based variance estimators that account for the complex clustering that can occur when interference is present. We also establish consistency and asymptotic normality under local dependence assumptions. We discuss refinements including covariate-adjusted effect estimators and ratio estimation. We evaluate empirical performance in realistic settings with a naturalistic simulation using social network data from American schools. We then present results from a field experiment on the spread of anti-conflict norms and behavior among school students.

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Research works printed but not linked: 4

  1. Aronow, P. M. (2013). Model Assisted Causal Inference. Ph.D. thesis, Dept. Political Science, Yale Univ., New Haven, CT.
  2. Basu, D. (1971). An essay on the logical foundations of survey sampling. I. In Foundations of Statistical Inference (V. Godambe and D. A. Sprott, eds.) 203–242. Holt, Rinehart and Winston, Toronto.
  3. Cox, D. R. (1958). Planning of Experiments. Wiley, New York.
  4. Manski, C. F. (1995). Identification Problems in the Social Sciences. Harvard Univ. Press, Cambridge, MA.

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Metadata

Authors
Aronow, P. M., & Samii, C.
Affiliations
Yale University (US)
Venue
The Annals of Applied Statistics
Publisher
Institute of Mathematical Statistics
Published
2017-12-01
Volume
11
Issue
4
Pages
1912-1947
ISSN
  • 1932-6157 (ISSN-L)
  • 1941-7330
DOI
10.1214/16-aoas1005
OpenAlex
W2735728227
Cited by
347
References
51
Citations by year
2026: 47 · 2025: 47 · 2024: 35 · 2023: 47 · 2022: 30 · 2021: 46 · 2020: 39 · 2019: 21 · 2018: 20 · 2017: 10
FWCI
17.12
Type
journal article
Language
en
Open access
Locations
  • The Annals of Applied Statistics (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Project Euclid (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
randomization inference, inverse probability weighting, variance estimation, local dependence, covariate adjustment, ratio estimation, school students

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