[Comparison of selected causality theories]

Abstract

Different research traditions have separately developed various theories of causal inference. Three of these approaches are contrasted in this paper. In particular, I will explore differences and similarities of the potential outcomes approach, the generalized causal inference framework of Campbell, and the theory of causal DAGs. Each of the 3 approaches has strengths and weaknesses; however I argue that it is possible to combine them in applied research. The potential outcomes approach offers an exact and formal definition of a causal effect, however it is not very informative as to which covariates need to be selected for adjustment. The Campbell framework lacks a precise mathematical definition of a causal effect, but offers a long list of threats to internal validity that researchers can try to rule out in their studies. Causal DAGs offer applied researchers a tool to find minimally sufficient adjustment sets of covariates that allow an unbiased estimation of a causal effect, given that researchers are willing and able to encode their causal assumptions of all important covariates.