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Virtually all journal articles in the factor investing literature make associational claims, in denial of the causal content of factor models. Authors do not identify the causal graph consistent with the observed phenomenon, they justify their chosen model specification in terms of correlations, and they do not propose experiments for falsifying causal mechanisms. Absent a causal theory, their findings are likely false, due to rampant backtest overfitting and incorrect specification choices. This Element differentiates between type-A and type-B spurious claims, and explains how both types prevent factor investing from advancing beyond its current phenomenological stage. It analyzes the current state of causal confusion in the factor investing literature, and proposes solutions with the potential to transform factor investing into a truly scientific discipline. This title is also available as Open Access on Cambridge Core.
Analytic studies are increasingly important for understanding relationships between diseases and their causes. They usually take the form of observational investigations, but can also include randomized clinical trials. Risk factors are antecedents that are considered to be components of the disease pathway. Appropriate study design and consideration to systematic bias and confounding helps to establish association between the exposure and disease. The purpose for maintaining the principles of causal inference and eliminating chance, bias, and confounding is to establish validity. The most serious concern in analytic studies is maintaining validity. Confounders are extraneous factors that are related to the disease and to a risk factor or exposure related to the disease. The confounder usually predicts disease in the absence of any risk factor. Investigators have to consider cost and efficiency in their design as well as the potential public health impact of any observed association.
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