A Review of Syllabi of University Courses Focusing on Causal Inference

• In Event: Educational Research From Different Lenses

Abstract

The syllabi of 29 university courses across 10 disciplines (e.g., Political Science, Education) focusing on causal inference were analyzed. The most frequently covered topics were propensity scores (90%), randomized experiments (86%), instrumental variables (83%), regression discontinuity designs (71%), and potential outcomes (70%). R was the most commonly used programming language for statistical computing (70%), and Mostly harmless econometrics: An empiricist's companion (Angrist& Pischke, 2008) was the most commonly used required textbook. These results provide one source of evidence of how instructors are responding to the theoretical, technical, and methodological developments in causal inference, and may provide guidance to instructors interested in developing or revising their courses on causal inference.

Authors

• Siyu Liu, University of South Florida

• Robert F. Dedrick, University of South Florida

• John M. Ferron, University of South Florida

• Eunsook Kim, University of South Florida

• Yi-Hsin Chen, University of South Florida

• Jennifer R. Wolgemuth, University of South Florida

• Myrna Veguilla

• Elif Topsakal

• Yue Yin, University of South Florida

• Gen Li, University of South Florida