A Cautionary Tale Regarding the Use of Causal Inference to Study How Environmental Change Influences Tropical Diseases

Abstract

There has been substantial interest on the effect of large-scale environmental change, such as deforestation, on human health. An important and relatively recent development has been the use of causal-inference approaches (e.g., instrumental variables [IVs]) to more properly analyze this type of observational data. Here, we discuss an important study that attempted to disentangle the effect of malaria on deforestation from the effect of deforestation on malaria using an IV approach. The authors found that deforestation increases malaria (e.g., they estimate that a 10% increase in deforestation leads to a 3.3% increase in malaria incidence) through ecological mechanisms, whereas malaria reduces deforestation through socioeconomic mechanisms. An important characteristic of causal-inference approaches is that they are critically dependent on the plausibility of the underlying assumptions and that, differently from standard statistical models, many of these assumptions are not testable. In particular, we show how important assumptions of the IV approach adopted in the study described earlier were not met and that, as a result, it is possible that the correct conclusion could have been the opposite of that reported by the authors (e.g., deforestation decreases, rather than increasing, malaria through ecological mechanisms). Causal-inference approaches may be critical to characterize the relationship between environmental change and disease risk, but conclusions based on these methods can be even more unreliable than those from traditional methods if careful attention is not given to the plausibility of the underlying assumptions.

Figure 1.

Panel A displays the causal diagram associated with a valid instrumental variable (IV) approach. Panel B shows the causal diagram for the example focused on the effect of schooling on wages. In this example, birthdate is used as an IV for schooling. Arrows reflect the direction of causation. This figure appears in color at www.ajtmh.org.

Figure 2.

Panels A and C illustrate the IV approach used by MacDonald and Mordecai while alternative diagrams that highlight the problems with their approach are given in panels B and D, respectively. Arrows reflect the direction of causation. The dashed line represents a potential causal path through which optimal temperature indirectly influences deforestation. This figure appears in color at www.ajtmh.org.