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

Lan Liu, Eric Tchetgen Tchetgen

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalBiometrics
Early online dateApr 29 2021
DOIs
StateE-pub ahead of print - Apr 29 2021

Bibliographical note

Funding Information:
The authors would like to give special thanks to Prof. O'Malley for insightful discussion and his patience and tremendous help on the data analysis section. The authors also thank the editor, associate editor and two reviewers for their insightful comments and helpful suggestions. Lan Liu's research is supported by NSF DMS 1916013.

Publisher Copyright:
© 2021 The International Biometric Society

Keywords

  • causal inference
  • collider
  • exogeneity
  • homophily
  • negative control exposure

PubMed: MeSH publication types

  • Journal Article

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