Visualizing Causal Hypotheses in Environmental Econometrics

Environmental economists have gravitated toward writing empirical papers with an emphasis on causal inference. Despite this development, there has not been much progress in adopting an explicit framework for communicating causal hypotheses based on prior beliefs about the structure of a data-generating process. This shortfall reduces the transparency and accessibility of the assumptions underlying effect identification and limits the feasibility of causal hypotheses testing. This article explains why an explicit framework is worthwhile and demonstrates how directed acyclic graphs can augment and standardize the communication of causal knowledge.