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Identifying the effects of environmental change on the transmission of vectorborne and zoonotic diseases is of fundamental importance in the face of rapid global change. Causal inference approaches, including instrumental variable (IV) estimation, hold promise in disentangling plausibly causal relationships from observational data in these complex systems. Valle and Zorello Laporta recently critiqued the application of such approaches in our recent study of the effects of deforestation on malaria transmission in the Brazilian Amazon on the grounds that key statistical assumptions were not met. Here, we respond to this critique by 1) deriving the IV estimator to clarify the assumptions that Valle and Zorello Laporta conflate and misrepresent in their critique, 2) discussing these key assumptions as they relate to our original study and how our original approach reasonably satisfies the assumptions, and 3) presenting model results using alternative instrumental variables that can be argued more strongly satisfy key assumptions, illustrating that our results and original conclusion—that deforestation drives malaria transmission—remain unchanged.
Financial support: A. J. M. and E. A. M. were supported by the National Science Foundation and the Fogarty International Center (DEB-2011147). E. A. M. was supported by the National Science Foundation (DEB-1518681), the National Institute of General Medical Sciences (R35GM133439), the Stanford King Center for Global Development, and the Terman Award.
Authors’ addresses: Andrew J. MacDonald, Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, E-mail: firstname.lastname@example.org. Erin A. Mordecai, Department of Biology, Stanford University, Stanford, CA, E-mail: email@example.com.