TY - JOUR
T1 - Causal Inference for Social Network Data
AU - Ogburn, Elizabeth L.
AU - Sofrygin, Oleg
AU - Díaz, Iván
AU - van der Laan, Mark J.
N1 - Publisher Copyright:
© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2024
Y1 - 2024
N2 - We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for dependence of each observation on a growing number of other units as sample size increases. In addition, while previous methods have implicitly permitted only one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information across network ties and for dependence due to latent similarities among nodes sharing ties. We propose new causal effects that are specifically of interest in social network settings, such as interventions on network ties and network structure. We use our methods to reanalyze an influential and controversial study that estimated causal peer effects of obesity using social network data from the Framingham Heart Study; after accounting for network structure we find no evidence for causal peer effects. Supplementary materials for this article are available online.
AB - We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for dependence of each observation on a growing number of other units as sample size increases. In addition, while previous methods have implicitly permitted only one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information across network ties and for dependence due to latent similarities among nodes sharing ties. We propose new causal effects that are specifically of interest in social network settings, such as interventions on network ties and network structure. We use our methods to reanalyze an influential and controversial study that estimated causal peer effects of obesity using social network data from the Framingham Heart Study; after accounting for network structure we find no evidence for causal peer effects. Supplementary materials for this article are available online.
KW - Causal inference
KW - Semiparametric inference
KW - Social networks
KW - Statistical dependence
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U2 - 10.1080/01621459.2022.2131557
DO - 10.1080/01621459.2022.2131557
M3 - Article
AN - SCOPUS:85140776453
SN - 0162-1459
VL - 119
SP - 597
EP - 611
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 545
ER -