Dr. Mahsa Kaviani
Assistant professor of finance at Temple University
Identification and inferring causality are among the major challenges in any empirical study. They influence how policymakers set policies, and how firms understand the outcomes of their actions. Identification and causality are therefore the centers of attention in many social science fields including corporate finance and banking as well as in development, labor, and health economics, etc. In this workshop we delve into these ideas: how do we infer causality? What are the methods that can help us infer causal effects? What are the challenges and threats to identification? And finally, how can we mitigate the influence of confounding effects? We review the sources of endogeneity—omitted variables, simultaneity, and measurement error—and their implications for inference. We then discuss a number of econometric techniques aimed at addressing endogeneity problems. Our discussion emphasizes on intuition and the applications of these methods in corporate finance.
Dr. Mahsa Kaviani is an assistant professor of finance at Temple University’s Fox School of Business in Philadelphia, PA. Prior to joining Temple, she was a visiting research scholar at the New York University, Stern School of Business’s finance department. As for her graduate studies, she holds an MA in Economics from McGill University with the concentration on economic development, and a Ph.D. in Finance from the joint program of Concordia, McGill, HEC, and UQAM in Montreal, Canada. Her main research interests are empirical corporate finance, banking and firm-bank relationships, and financial distress. Her works have been accepted in major finance conferences (including AFA, EFA and NFA) and are under review at top finance journals.