Causal inference, social networks and chain graphs

Elizabeth L. Ogburn, Ilya Shpitser, Youjin Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Traditionally, statistical inference and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social networks, where individuals may interact with one another such that treatments may spill over from the treated individual to their social contacts and outcomes may be contagious. Existing models proposed for causal inference using observational data from networks of interacting individuals have two major shortcomings. First, they often require a level of granularity in the data that is infeasible in practice to collect in most settings and, second, the models are high dimensional and often too big to fit to the available data. We illustrate and justify a parsimonious parameterization for network data with interference and contagion. Our parameterization corresponds to a particular family of graphical models known as chain graphs. We argue that, in some settings, chain graph models approximate the marginal distribution of a snapshot of a longitudinal data-generating process on interacting units. We illustrate the use of chain graphs for causal inference about collective decision making in social networks by using data from US Supreme Court decisions between 1994 and 2004 and in simulations.

Original languageEnglish (US)
Pages (from-to)1659-1676
Number of pages18
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
Volume183
Issue number4
DOIs
StatePublished - Oct 1 2020

Keywords

  • Causal inference
  • Chain graphs
  • Collective behaviour
  • Graphical models
  • Social networks

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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