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Bias and High-Dimensional Adjustment in Observational Studies of Peer Effects

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  • Dean Eckles
  • Eytan Bakshy

Abstract

Peer effects, in which an individual’s behavior is affected by peers’ behavior, are posited by multiple theories in the social sciences. Randomized field experiments that identify peer effects, however, are often expensive or infeasible, so many studies of peer effects use observational data, which is expected to suffer from confounding. Here we show, in the context of information and media diffusion, that high-dimensional adjustment of a nonexperimental control group (660 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those using a large randomized experiment (215 million observations). Compared with the experiment, naive observational estimators overstate peer effects by over 300% and commonly available variables (e.g., demographics) offer little bias reduction. Adjusting for a measure of prior behaviors closely related to the focal behavior reduces this bias by 91%, while models adjusting for over 3700 past behaviors provide additional bias reduction, reducing bias by over 97%, which is statistically indistinguishable from unbiasedness. This demonstrates how detailed records of behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of peer effects in rare or new behaviors. More generally, these results show how large, high-dimensional datasets and statistical learning can be used to improve causal inference. Supplementary materials for this article are available online.

Suggested Citation

  • Dean Eckles & Eytan Bakshy, 2021. "Bias and High-Dimensional Adjustment in Observational Studies of Peer Effects," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 507-517, April.
  • Handle: RePEc:taf:jnlasa:v:116:y:2021:i:534:p:507-517
    DOI: 10.1080/01621459.2020.1796393
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    Cited by:

    1. Pablo Geraldo Bast'ias, 2024. "Credible causal inference beyond toy models," Papers 2402.11659, arXiv.org.
    2. Alexander Lavin & Ciarán M. Gilligan-Lee & Alessya Visnjic & Siddha Ganju & Dava Newman & Sujoy Ganguly & Danny Lange & Atílím Güneş Baydin & Amit Sharma & Adam Gibson & Stephan Zheng & Eric P. Xing &, 2022. "Technology readiness levels for machine learning systems," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    3. Yi Cao & Tao Zhou & Jian Gao, 2024. "Heterogeneous peer effects of college roommates on academic performance," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. Yan Leng & Xiaowen Dong & Esteban Moro & Alex Pentland, 2024. "Long-Range Social Influence in Phone Communication Networks on Offline Adoption Decisions," Information Systems Research, INFORMS, vol. 35(1), pages 318-338, March.

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