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Regression‐based negative control of homophily in dyadic peer effect analysis

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  • Lan Liu
  • Eric Tchetgen Tchetgen

Abstract

A prominent threat to causal inference about peer effects in social science studies is the presence of homophily bias , that is, social influence between friends and families is entangled with common characteristics or underlying similarities that form close connections. Analysis of social study data has suggested that certain health conditions such as obesity and psychological states including happiness and loneliness can spread between friends and relatives. However, such analyses of peer effects or contagion effects have come under criticism because homophily bias may compromise the causal statement. We develop a regression‐based approach which leverages a negative control exposure for identification and estimation of contagion effects on additive or multiplicative scales, in the presence of homophily bias. We apply our methods to evaluate the peer effect of obesity in Framingham Offspring Study.

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  • Lan Liu & Eric Tchetgen Tchetgen, 2022. "Regression‐based negative control of homophily in dyadic peer effect analysis," Biometrics, The International Biometric Society, vol. 78(2), pages 668-678, June.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:2:p:668-678
    DOI: 10.1111/biom.13483
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    References listed on IDEAS

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    1. Braz Camargo & Ralph Stinebrickner & Todd Stinebrickner, 2010. "Interracial Friendships in College," Journal of Labor Economics, University of Chicago Press, vol. 28(4), pages 861-892, October.
    2. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    3. Sobel, Michael E., 2006. "What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1398-1407, December.
    4. A. James O'Malley & Felix Elwert & J. Niels Rosenquist & Alan M. Zaslavsky & Nicholas A. Christakis, 2014. "Estimating peer effects in longitudinal dyadic data using instrumental variables," Biometrics, The International Biometric Society, vol. 70(3), pages 506-515, September.
    5. Felix Elwert & Nicholas Christakis, 2008. "Wives and ex-wives: A new test for homogamy bias in the widowhood effect," Demography, Springer;Population Association of America (PAA), vol. 45(4), pages 851-873, November.
    6. Giacomo De Giorgi & Michele Pellizzari & Silvia Redaelli, 2010. "Identification of Social Interactions through Partially Overlapping Peer Groups," American Economic Journal: Applied Economics, American Economic Association, vol. 2(2), pages 241-275, April.
    7. L. Liu & M. G. Hudgens & S. Becker-Dreps, 2016. "On inverse probability-weighted estimators in the presence of interference," Biometrika, Biometrika Trust, vol. 103(4), pages 829-842.
    8. Lan Liu & Michael G. Hudgens, 2014. "Large Sample Randomization Inference of Causal Effects in the Presence of Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 288-301, March.
    9. Hudgens, Michael G. & Halloran, M. Elizabeth, 2008. "Toward Causal Inference With Interference," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 832-842, June.
    10. Fowler, J.H. & Christakis, N.A., 2008. "Estimating peer effects on health in social networks: A response to Cohen-Cole and Fletcher; and Trogdon, Nonnemaker, and Pais," Journal of Health Economics, Elsevier, vol. 27(5), pages 1400-1405, September.
    11. Wang Miao & Zhi Geng & Eric J Tchetgen Tchetgen, 2018. "Identifying causal effects with proxy variables of an unmeasured confounder," Biometrika, Biometrika Trust, vol. 105(4), pages 987-993.
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