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

26 Pages Posted: 2 May 2015

Date Written: January 23, 2015

Abstract

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.

Keywords: Graphical Causal Models; Causal Inference; Econometric Identification; Directed Acyclical Graphs

JEL Classification: C10, M40

Suggested Citation

Kallapur, Sanjay, Econometric Identification of Causal Effects: A Graphical Approach with a Case Study (January 23, 2015). Indian School of Business Research Paper Series, Available at SSRN: https://ssrn.com/abstract=2601276 or http://dx.doi.org/10.2139/ssrn.2601276

Sanjay Kallapur (Contact Author)

Indian School of Business ( email )

ISB Campus, Gachibowli
Hyderabad, 500 032
India
+91 40 2318 7138 (Phone)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
114
Abstract Views
811
rank
345,789
PlumX Metrics