Article ID: 2305
Abstract: Statistical causal inference methods have become widespread in epidemiology and the social sciences. Some, such as multiple regression analysis and generalised linear models, complement the methods traditionally used in ecology and have gained popularity in that field. Others remain largely unfamiliar to ecologists. Among these, this paper describes the propensity score method, disjunctive regression design, and the instrumental variable method, all widely used in epidemiology and the social sciences. The propensity score method estimates causal effects without bias by constructing a single synthetic variable that expresses the probability, or “propensity”, of a unit of interest being assigned to a given treatment based on multiple background factors. In conservation ecology, there are various situations where propensity scores may be appropriate, such as when estimating the causal effects of a binary conservation intervention. Where the objectives and circumstances fit, the propensity score method can be a powerful way to adjust for background factors in a single step. The regression discontinuity design, by contrast, estimates causal effects by estimating discontinuous changes in the regression line at the boundaries of the treatment change. Similarly, the instrumental variable method estimates causal effects using so-called instrumental variables that bring about changes from outside the system. There may be relatively few situations in ecology where the latter two methods can be effectively applied. However, awareness of these methods increases the range of options available when developing survey designs and statistical strategies. If and when one of these methods is applied, it will serve as a pioneering example in ecology.
