Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks

Abstract-Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference. In the presence of interference, for instance, potential outcomes of a unit depend on their treatment as well as on the treatments of other u...

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Bibliographic Details
Published in:Journal of the American Statistical Association 2021-04, Vol.116 (534), p.901-918
Main Authors: Forastiere, Laura, Airoldi, Edoardo M., Mealli, Fabrizia
Format: Article
Language:English
Subjects:
Bias
Causal inference
Generalized propensity scores
Inference
Interference
Mathematical analysis
Neighborhoods
Network interference
Networks
Observational studies
Potential outcomes
Propensity
Regression analysis
Statistical methods
Statistics
Subclassification
Unconfoundedness
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Summary:Abstract-Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference. In the presence of interference, for instance, potential outcomes of a unit depend on their treatment as well as on the treatments of other units, such as their neighbors in the network. In observational studies, a further complication is that the typical unconfoundedness assumption must be extended-say, to include the treatment of neighbors, and individual and neighborhood covariates-to guarantee identification and valid inference. Here, we propose new estimands that define treatment and interference effects. We then derive analytical expressions for the bias of a naive estimator that wrongly assumes away interference. The bias depends on the level of interference but also on the degree of association between individual and neighborhood treatments. We propose an extended unconfoundedness assumption that accounts for interference, and we develop new covariate-adjustment methods that lead to valid estimates of treatment and interference effects in observational studies on networks. Estimation is based on a generalized propensity score that balances individual and neighborhood covariates across units under different levels of individual treatment and of exposure to neighbors' treatment. We carry out simulations, calibrated using friendship networks and covariates in a nationally representative longitudinal study of adolescents in grades 7-12 in the United States, to explore finite-sample performance in different realistic settings. Supplementary materials for this article are available online.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.2020.1768100