A definition of the causal effect of a political party's nominee on the U.S. general presidential election using counterfactual response types.

The electability of the candidates for the 2020 Democratic U.S. presidential nomination was frequently debated. Arguments regarding a candidate's electability often claimed that they would affect the general election by changing the behavior of a certain subset of eligible voters. For example, is it more important electorally that a candidate drive turnout or swing voting? As lay consumers of political opinion, we were having difficulty weighing these questions from a strategic standpoint. Although candidate electability is a nebulous term that might be interpreted in various ways, one interpretation of the term is a population-based causal question: What would the effect of the Democratic nominee be on the presidential election result? Population-based causal questions are commonly studied in epidemiology. To aid interpretation of electability arguments, we frame the question through a counterfactual model used in epidemiology. Specifically, we define the causal effect by characterizing the population of eligible voters into nine counterfactual response types. The definition clarifies our ability to interpret arguments regarding the electability of the candidates. For example, the causal effect can be subdivided into three parts: the effect of the nominee on (1) Democratic turnout, (2) Republican turnout, and (3) swing voting. We show using notation that the third part has twice the weight as the other two. The definition follows intuition. However, we hope its formalization using counterfactual response types may foster interdisciplinary communication.