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.