Revisiting the effects of maternal education on adolescents' academic performance: Doubly robust estimation in a network-based observational study

McNealis, V., Moodie, E. E.M. and Dean, N. (2024) Revisiting the effects of maternal education on adolescents' academic performance: Doubly robust estimation in a network-based observational study. Journal of the Royal Statistical Society: Series C (Applied Statistics), (doi: 10.1093/jrsssc/qlae008) (Early Online Publication)

Abstract

In many contexts, particularly when study subjects are adolescents, peer effects can invalidate typical statistical requirements in the data. For instance, it is plausible that a student’s academic performance is influenced both by their own mother’s educational level as well as that of their peers. Since the underlying social network is measured, the Add Health study provides a unique opportunity to examine the impact of maternal college education on adolescent school performance, both direct and indirect. However, causal inference on populations embedded in social networks poses technical challenges, since the typical no interference assumption no longer holds. While inverse probability-of-treatment weighted (IPW) estimators have been developed for this setting, they are often highly unstable. Motivated by the question of maternal education, we propose doubly robust (DR) estimators combining models for treatment and outcome that are consistent and asymptotically normal if either model is correctly specified. We present empirical results that illustrate the DR property and the efficiency gain of DR over IPW estimators even when the treatment model is misspecified. Contrary to previous studies, our robust analysis does not provide evidence of an indirect effect of maternal education on academic performance within adolescents’ social circles in Add Health.

• Item Type: Articles
• Additional Information: This research was enabled in part by support provided by Calcul Québec (https://www. calculquebec.ca/) and the Digital Research Alliance of Canada (https://alliancecan.ca/en). The case study in this article uses data from Add Health, funded by grant P01 HD31921 (Harris) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), with cooperative funding from 23 other federal agencies and foundations. Add Health is currently directed by Robert A. Hummer and funded by the National Institute on Aging cooperative agreements U01 AG071448 (Hummer) and U01AG071450 (Aiello and Hummer) at the University of North Carolina at Chapel Hill. Vanessa McNealis is supported by doctoral fellowships from Natural Sciences and Engineering Research Council of Canada (NSERC) and the Fonds de Recherche du Québec (FRQ)—Nature et Technologie. Erica E. M. Moodie acknowledges support from an NSERC Discovery Grant. Erica E. M. Moodie is a Canada Research Chair (Tier 1) in Statistical Methods for Precision Medicine and acknowledges the support of a Chercheur-boursier de mérite career award from the FRQ—Santé.
• Keywords: Causal Inference, doubly robust methods, network interference, observational studies.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Dean, Dr Nema
• Authors: McNealis, V., Moodie, E. E.M., and Dean, N.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Journal of the Royal Statistical Society: Series C (Applied Statistics)
• Publisher: Oxford University Press
• ISSN: 0035-9254
• ISSN (Online): 1467-9876
• Published Online: 13 February 2024
• Copyright Holders: Copyright: © The Royal Statistical Society 2024
• First Published: First published in Journal of the Royal Statistical Society: Series C (Applied Statistics) 2024
• Publisher Policy: Reproduced in accordance with the publisher copyright policy