Generalized propensity score approach to causal inference with spatial interference.
air pollution
causal inference
interference
spatial process
wildfire
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
22
11
2020
accepted:
09
08
2022
medline:
13
9
2023
pubmed:
24
8
2022
entrez:
23
8
2022
Statut:
ppublish
Résumé
Many spatial phenomena exhibit interference, where exposures at one location may affect the response at other locations. Because interference violates the stable unit treatment value assumption, standard methods for causal inference do not apply. We propose a new causal framework to recover direct and spill-over effects in the presence of spatial interference, taking into account that exposures at nearby locations are more influential than exposures at locations further apart. Under the no unmeasured confounding assumption, we show that a generalized propensity score is sufficient to remove all measured confounding. To reduce dimensionality issues, we propose a Bayesian spline-based regression model accounting for a sufficient set of variables for the generalized propensity score. A simulation study demonstrates the accuracy and coverage properties. We apply the method to estimate the causal effect of wildland fires on air pollution in the Western United States over 2005-2018.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2220-2231Subventions
Organisme : NIEHS NIH HHS
ID : R01 ES031651
Pays : United States
Informations de copyright
© 2022 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society.
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