On falsification of the binary instrumental variable model.

Area of Science:

• Econometrics
• Biostatistics
• Causal Inference

Background:

• Instrumental variables (IV) are crucial for estimating causal effects when unmeasured confounding is present.
• Current IV validity assessments often rely on subject-matter expertise rather than empirical tests.
• A lack of formal statistical tests with known properties hinders the assessment of IV exogeneity.

Purpose of the Study:

• To develop simple, formal statistical procedures for testing the validity of binary instrumental variable models.
• To provide methods that leverage testable implications of the discrete instrumental variable model.
• To illustrate the practical importance of testing instrumental variable assumptions.

Main Methods:

• Development of novel procedures for testing binary instrumental variable models.
• Adaptation of existing statistical techniques, specifically the chi-squared test and the Gail-Simon test.
• Application of these tests to assess the exogeneity of college proximity as an instrumental variable.

Main Results:

• The study presents practical methods for empirically testing instrumental variable assumptions.
• The proposed tests offer a formal alternative to reliance solely on subject-matter arguments for IV validity.
• Evaluation using the National Longitudinal Survey of Young Men demonstrates the utility of the testing procedures.

Conclusions:

• Formal statistical testing of instrumental variable models is feasible and essential for robust causal inference.
• The developed procedures enhance the reliability of causal effect estimations by validating instrumental variables.
• This work provides a foundation for more rigorous application of instrumental variable methods in various scientific fields.