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The omission of relevant explanatory variables in a regression model generally causes its estimators to be biased. This issue is referred to as omitted variable bias (OVB) and is recognized as one of the primary sources of endogeneity. In turn, the concern of OVB is often a key motivating reason for adopting instrumental variable techniques. These techniques typically involve a two-step procedure that constructs a version of the independent variable that does not feature variance due to the omitted variable. While these techniques can help alleviate the OVB concern, they also have critical assumptions that must be met regarding the instrumental variables employed (i.e., relevance and exogeneity). Even when these assumptions are met, instrumental variable techniques are often less efficient than ordinary least square regression. Recently, the impact threshold of a confounding variable (ITCV) has been introduced in organization research. The ITCV can be used to understand whether a statistical inference is changed because of the potential for an omitted variable. In this talk, the issue of OVB will be formally defined, and both instrumental variable techniques and the ITCV will be discussed as ways to help alleviate this concern.

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