Social Networks and Causal Inference

Chapter
Part of the Handbooks of Sociology and Social Research book series (HSSR)

Abstract

This chapter reviews theoretical developments and empirical studies related to causal inference on social networks from both experimental and observational studies. Discussion is given to the effect of experimental interventions on outcomes and behaviors and how these effects relate to the presence of social ties, the position of individuals within the network, and the underlying structure and properties of the network. The effects of such experimental interventions on changing the network structure itself and potential feedback between behaviors and network changes are also discussed. With observational data, correlations in behavior or outcomes between individuals with network ties may be due to social influence, homophily, or environmental confounding. With cross-sectional data these three sources of correlation cannot be distinguished. Methods employing longitudinal observational data that can help distinguish between social influence, homophily, and environmental confounding are described, along with their limitations. Proposals are made regarding future research directions and methodological developments that would help put causal inference on social networks on a firmer theoretical footing.

Keywords

Social Network Social Influence Causal Inference Network Effect Social Network Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors thank Stephen Morgan for helpful comments. This work was supported by NIH grant ES017876.

References

  1. An, W. (2011a). Models and methods to identify peer effects. In J. Scott & P. J. Carrington (Eds.), The Sage handbook of social network analysis (pp. 514–532). London: The Sage Publications.Google Scholar
  2. An, W. (2011b). Peer effects on adolescent smoking and social network-based interventions. PhD dissertation, Department of Sociology, Harvard University.Google Scholar
  3. An, W. (2011c). Instrumental variable estimates of peer effects. Working paper, Department of Sociology, Harvard University.Google Scholar
  4. An, W. (2011d). Algorithms for social network-based interventions and policies. Working paper, Department of Sociology, Harvard University.Google Scholar
  5. An, W. (2011e). On the directionality test of peer effects. Working paper, Department of Sociology, Harvard University.Google Scholar
  6. Anagnostopoulos, A., Kumar, R., & Mahdian, M. (2008). Influence and correlation in social networks. In Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 7–15). New York: ACM.Google Scholar
  7. Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton: Princeton University Press.Google Scholar
  8. Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29, 555–564.CrossRefGoogle Scholar
  9. Borgatti, S. P. (2005). Creating knowledge: Network structure and innovation. Available at http://www.socialnetworkanalysis.com/knowledge_creation.htm
  10. Borgatti, S. P. (2006). Identifying sets of key players in a network. Computational, Mathematical and Organizational Theory, 12(1), 21–34.CrossRefGoogle Scholar
  11. Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150(1), 41–55.CrossRefGoogle Scholar
  12. Burt, R. S. (1987). Social contagion and innovation: Cohesion versus structural equivalence. American Journal of Sociology, 92(6), 1287–1335.CrossRefGoogle Scholar
  13. Burt, R. S. (1995). Structural holes: The social structure of competition. Cambridge: Harvard University Press.Google Scholar
  14. Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology, 110, 349–399.CrossRefGoogle Scholar
  15. Cacioppo, J. T., Fowler, J. H., & Christakis, N. A. (2009). Alone in the crowd: The structure and spread of loneliness in a large social network. Journal of Personality and Social Psychology, 97(6), 977–991.CrossRefGoogle Scholar
  16. Callahan, O. D., & Robin, S. S. (1969). A social system analysis of preferred leadership role characteristics in high school. Sociology of Education, 42(3), 251–260.CrossRefGoogle Scholar
  17. Camargo, B., Stinebrickner, R., & Stinebrickner, T. R. (2010). Interracial friendships in college (NBER Working Paper No. 15970). http://www.nber.org/papers/w15970.pdf
  18. Campbell, R., Starkey, F., Holliday, J., Audrey, S., Bloor, M., Parry-Langdon, N., Hughes, R., & Moore, L. (2008). An informal school-based peer-led intervention for smoking prevention in adolescence (ASSIST): A cluster randomised trial. Lancet, 371, 1595–1602.CrossRefGoogle Scholar
  19. Carrington, P. J., Scott, J., & Wasserman, S. (2005). Model and methods in social network analysis. New York: Cambridge University Press.CrossRefGoogle Scholar
  20. Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329, 1194–1197.CrossRefGoogle Scholar
  21. Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357, 370–379.CrossRefGoogle Scholar
  22. Christakis, N. A., & Fowler, J. H. (2008). The collective dynamics of smoking in a large social network. New England Journal of Medicine, 358, 2249–2258.CrossRefGoogle Scholar
  23. Christakis, N. A., & Fowler, J. H. (2013). Social contagion theory: Examining dynamic social networks and human behavior. Statistics in Medicine 32(4), 556–577.Google Scholar
  24. Cipollone, P., & Rosolia, A. (2007). Social interactions in high school: Lessons from an earthquake. The American Economic Review, 97(3), 948–965.CrossRefGoogle Scholar
  25. Cohen-Cole, E., & Fletcher, J. M. (2008). Detecting implausible social network effects in acne, height, and headaches: Longitudinal analysis. British Medical Journal, 337, a2533.CrossRefGoogle Scholar
  26. Copic, J., Jackson, M. O., & Kirman, A. (2009). Identifying community structures from network data via maximum likelihood methods. The B.E. Journal of Theoretical Economics, 9(1), Article 30.Google Scholar
  27. Cornwell, B. (2009). Good health and the bridging of structural holes. Social Networks, 31, 92–103.CrossRefGoogle Scholar
  28. Cox, D. R. (1958). The planning of experiments. New York: Wiley.Google Scholar
  29. Dawber, T. R. (1980). The Framingham study: The epidemiology of atherosclerotic disease. Cambridge: Harvard University Press.Google Scholar
  30. Desmarais, B. A., & Cranmer, S. J. (2012). Statistical inference for valued-edge networks: The generalized exponential random graph model. PLoS ONE, 1(7), e30136.CrossRefGoogle Scholar
  31. Duflo, E., & Saez, E. (2003). The role of information and social interactions in retirement plan decisions: Evidence from a randomized experiment. The Quarterly Journal of Economics, 118(3), 815–842.CrossRefGoogle Scholar
  32. Duncan, O. D., Haller, A. O., & Portes, A. (1968). Peer influences on aspirations: A reinterpretation. The American Journal of Sociology, 74(2), 119–137.CrossRefGoogle Scholar
  33. Falk, A., & Ichino, A. (2006). Clean evidence on peer effects. Journal of Labor Economics, 24(1), 39–56.CrossRefGoogle Scholar
  34. Feinleib, M., Kannel, W. B., Garrison, R. J., McNamara, P. M., & Castelli, W. P. (1975). The Framingham offspring study: Design and preliminary data. Preventive Medicine, 4, 518–525.CrossRefGoogle Scholar
  35. Fowler, J. H., & Christakis, N. A. (2008). Estimating peer effects on health in social networks. Journal of Health Economics, 27(5), 1386–1391.CrossRefGoogle Scholar
  36. Fowler, J. H., & Christakis, N. A. (2010). Cooperative behavior cascades in human social networks. PNAS: Proceedings of the National Academy of Sciences, 107, 5334–5338.CrossRefGoogle Scholar
  37. Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of National Academy of Sciences of the United States of America, 99(12), 7821–7826.CrossRefGoogle Scholar
  38. Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2009). A survey of statistical network models. Foundations and Trends in Machine Learning, 2, 129–233.CrossRefGoogle Scholar
  39. Graham, B. (2008). Identifying social interactions through conditional variance restrictions. Econometrica, 76, 643–660.CrossRefGoogle Scholar
  40. Halloran, M. E., & Struchiner, C. J. (1991). Study designs for dependent happenings. Epidemiology, 2, 331–338.CrossRefGoogle Scholar
  41. Hernán, M. A., & VanderWeele, T. J. (2011). Compound treatments and transportability of causal inference. Epidemiology, 22, 368–377.CrossRefGoogle Scholar
  42. Hudgens, M. G., & Halloran, M. E. (2008). Towards causal inference with interference. Journal of the American Statistical Association, 103, 832–842.CrossRefGoogle Scholar
  43. Hong, G., & Raudenbush, S. W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multilevel observational data. Journal of the American Statistical Association, 101, 901–910.CrossRefGoogle Scholar
  44. Kremer, M., & Levy, D. (2008). Peer effects and alcohol use among college students. Journal of Economic Perspectives, 22(3), 189–206.CrossRefGoogle Scholar
  45. Lee, L.-f. (2009). Identification and estimation of spatial econometric models with group interactions, contextual factors and fixed effects. Journal of Econometrics, 140(2), 333–374.CrossRefGoogle Scholar
  46. Lyons, R. (2011). The spread of evidence-poor medicine via flawed social-network analyses. Statistics, Politics and Policy, 2(1), Article 2, 1–26.Google Scholar
  47. Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. Review of Economic Studies, 60, 531–542.CrossRefGoogle Scholar
  48. Manski, C. F. (2013). Identification of treatment response with social interactions. The Econometric Journal 16(1), S1–S23.Google Scholar
  49. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444.CrossRefGoogle Scholar
  50. Moody, J. (2001). Peer influence groups: Identifying dense clusters in large networks. Social Networks, 23, 261–283.CrossRefGoogle Scholar
  51. Morgan, S. L., & Sørensen, A. B. (1999). Parental networks, social closure, and mathematics learning: A test of Coleman’s social capital explanation of school effects. American Sociological Review, 64, 661–681.CrossRefGoogle Scholar
  52. Morgan, S. L., & Todd, J. J. (2009). Intergenerational closure and academic achievement in high school: A new evaluation of Coleman’s conjecture. Sociology of Education, 82(July), 267–286.CrossRefGoogle Scholar
  53. Noel, H., & Nyhan, B. (2011). The ‘unfriending’ problem: The consequences of homophily in friendship retention for causal estimates of social influence. Social Networks, 33, 211–218.CrossRefGoogle Scholar
  54. Ohtsuki, H., Hauert, C., Lieberman, E., & Nowak, M. A. (2006). A simple rule for the evolution of cooperation on graphs and social networks. Nature, 441, 502–505.CrossRefGoogle Scholar
  55. O’Malley, A. J., Elwert, F., Rosenquist, J. N., Zaslavsky, A. M., & Christakis, N. A. (2013). Estimating peer effects in longitudinal dyadic data using instrumental variables (Working Paper). Department of Health Care Policy, Harvard Medical School.Google Scholar
  56. Pacheco, J. M., Traulsen, A., & Nowak, M. A. (2006). Coevolution of strategy and structure in complex networks with dynamical linking. Physical Review Letters, 97(25), 258103.CrossRefGoogle Scholar
  57. Podolny, J. M. (2001). Networks as the pipes and prisms of the market. American Journal of Sociology, 107, 33–60.CrossRefGoogle Scholar
  58. Portes, A. (1998). Social capital: Its origins and applications in modern sociology. Annual Review of Sociology, 24, 1–24.CrossRefGoogle Scholar
  59. Rosenbaum, P. R. (2007). Interference between units in randomized experiments. Journal of the American Statistical Association, 102, 191–200.CrossRefGoogle Scholar
  60. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology, 66, 688–701.CrossRefGoogle Scholar
  61. Rubin, D. B. (1980). Comment on: ‘Randomization analysis of experimental data in the Fisher randomization test’ by D. Basu. Journal of the American Statistical Association, 75, 591–593.Google Scholar
  62. Sacerdote, B. (2001). Peer effects with random assignment: Results for Dartmouth roommates. Quarterly Journal of Economics, 116, 681–704.CrossRefGoogle Scholar
  63. Shalizi, C. R., & Thomas, A. C. (2011). Homophily and contagion are generically confounded in observational social network studies. Sociological Methods and Research, 40, 211–239.CrossRefGoogle Scholar
  64. Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31, 361–395.CrossRefGoogle Scholar
  65. Snijders, T. A. B. (2005). Models for longitudinal network data. In P. J. Carrington, J. Scott, & S. S. Wasserman (Eds.), Models and methods in social network analysis. New York: Cambridge University Press. Chap. 11.Google Scholar
  66. Sobel, M. E. (2006). What do randomized studies of housing mobility demonstrate? Causal inference in the face of interference. Journal of the American Statistical Association, 101, 1398–1407.CrossRefGoogle Scholar
  67. Steglich, C. E., Snijders, T. A., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40, 329–393.CrossRefGoogle Scholar
  68. Tchetgen, T., Eric, J., & VanderWeele, T. J. (2012). On causal inference in the presence of interference. Statistical Methods in Medical Research – Special Issue on Causal Inference, 21, 55–75.CrossRefGoogle Scholar
  69. Uzzi, B., & Spiro, J. (2005). Collaboration and creativity: The small world problem. American Journal of Sociology, 111(2), 447–504.CrossRefGoogle Scholar
  70. Valente, T. W. (2005). Network models and methods for studying the diffusion of innovations. In P. Carrington, J. Scott, & S. Wasserman (Eds.), Models and methods in social network analysis (pp. 98–116). New York: Cambridge University Press.CrossRefGoogle Scholar
  71. Valente, T. W., & Davis, R. L. (1999). Accelerating the diffusion of innovations using opinion leaders. The ANNALS of the American Academy of Political and Social Science, 566, 55–67.CrossRefGoogle Scholar
  72. Valente, T. W., & Pumpuang, P. (2007). Identifying opinion leaders to promote behavior change. Health Education and Behavior, 34, 881–896.CrossRefGoogle Scholar
  73. Valente, T. W., Hoffman, B. R., Ritt-Olson, A., Lichtman, K., & Johnson, C. A. (2003). Effects of a social-network method for group assignment strategies on peer-led tobacco prevention programs in schools. American Journal of Public Health, 93(1), 1837–1843.CrossRefGoogle Scholar
  74. VanderWeele, T. J. (2011). Sensitivity analysis for contagion effects in social networks. Sociological Methods and Research, 40, 240–255.CrossRefGoogle Scholar
  75. VanderWeele, T. J., & Arah, O. A. (2011). Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments and confounders. Epidemiology, 22, 42–52.CrossRefGoogle Scholar
  76. VanderWeele, T. J., & Tchetgen Tchetgen, E. J. (2011). Effect partitioning under interference for two-stage randomized experiments. Statistics and Probability Letters, 81, 861–869.CrossRefGoogle Scholar
  77. VanderWeele, T. J., Ogburn, E. L., & Tchetgen Tchetgen, E. J. (2012a). Why and when “flawed” social network analyses still yield valid tests of no contagion. Statistics, Politics, and Policy, 3, Article 4, 1–11.Google Scholar
  78. VanderWeele, T. J., Tchetgen Tchetgen, E. J., & Halloran, M. E. (2012b). Components of the indirect effect in vaccine trials: Identification of contagion and infectiousness effects. Epidemiology, 23, 751–761.Google Scholar
  79. Vissa, B. (2011). A matching theory of entrepreneurs’ tie formation intentions and initiation of economic exchange. Academy of Management Journal, 54(1), 137–158.CrossRefGoogle Scholar
  80. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press.CrossRefGoogle Scholar
  81. Wing, R. R., & Jeffery, R. W. (1999). Benefits of recruiting participants with friends and increasing social support for weight loss and maintenance. Journal of Consulting and Clinical Psychology, 67(1), 132–138.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Departments of Epidemiology and BiostatisticsHarvard UniversityBostonUSA
  2. 2.Departments of Sociology and StatisticsIndiana UniversityBloomingtonUSA

Personalised recommendations