Can we make smart choices between OLS and contaminated IV methods?

Area of Science:

• Econometrics
• Statistical Inference
• Health Services Research

Background:

• Instrumental variables (IVs) are crucial for causal inference in outcomes research.
• Cautionary tales often discourage IV use due to imperfect instruments, overlooking their benefits.
• The potential of IVs to yield superior results compared to ordinary least squares (OLS) is underappreciated.

Purpose of the Study:

• To propose a diagnostic criterion and software to assess the plausible superiority of IV over OLS estimators.
• To aid applied researchers in determining if IV methods offer a better estimate than OLS when dealing with endogeneity.
• To provide a method for evaluating IV effectiveness without needing to know the exact level of instrument contamination.

Main Methods:

• Development of a diagnostic criterion for comparing IV and OLS estimators.
• Creation of accompanying software to implement the diagnostic criterion.
• Utilizing a predefined lower bound for OLS bias to evaluate IV performance.

Main Results:

• The proposed diagnostic tool enables researchers to determine if an IV estimator is likely to have a lower mean squared error than an OLS estimator.
• The method allows for the assessment of IV superiority even when the instrument is not econometrically perfect.
• The tool provides a practical approach for applied researchers to choose between IV and OLS methods.

Conclusions:

• Instrumental variables remain a valuable tool for causal inference in applied research, even with non-ideal instruments.
• The developed diagnostic criterion and software offer practical guidance for researchers to confidently apply IV methods.
• This approach can lead to more reliable effect estimates in outcomes and comparative effectiveness research by appropriately selecting between IV and OLS.