Econometric Identification of Causal Effects: A Graphical Approach with a Case Study

It is well known that causal inference relies on untestable a-priori causal assumptions. Identification refers to whether a causal relationship can be inferred from observed statistical associations; it requires an understanding of what statistical associations are induced by those causal assumptions. Since the assumptions are untestable, a transparent description of their statistical consequences helps the readers. However, the relation between causal assumptions and their induced statistical associations may not be obvious. In this paper I describe a technique known as Directed Acyclical Graphs or Graphical Bayesian Network or Graphical Causal Models. The technique was developed in the computer science literature in the 1980s (Pearl 2009) although it has antecedents in path analysis developed by Philip and Sewall Wright beginning in the 1920s (Wright 1921). In addition to describing the technique, I illustrate its application to a case study of a research issue in auditing.