Overlooked biases from misidentifications of causal structures

Abstract

Testing theories and explaining phenomena in empirical finance often requires estimating causal effects from observational data. In this note, we argue that some of the standard practices to address endogeneity concerns in regression-based estimation approaches can, when not correctly implemented and their results not appropriately interpreted, generate additional, often overlooked, problems. We identify three main systemic issues in empirical finance, provide theoretical and numerical examples to illustrate and support our arguments, and propose solutions to overcome these limitations. Overall, we suggest that these issues are caused by a systematic underestimation of the importance of robust ex-ante identification, and interpretation, of causal structures in empirical studies in finance.