Rethinking Nonlinear Instrumental Variable Models through Prediction Validity.

Area of Science:

• Econometrics
• Machine Learning
• Causal Inference

Background:

• Instrumental variables (IV) are crucial for estimating causal effects in observational studies when experiments are not feasible.
• Valid IV inference relies on relevance and exclusion restriction assumptions, often assumed rather than verified.
• Current methods lack empirical validation for these critical IV assumptions.

Purpose of the Study:

• To develop a machine learning-based framework for validating instrumental variable assumptions.
• To provide researchers with empirical evidence on instrument quality using data.
• To enhance the reliability of causal inference in social and health sciences.

Main Methods:

• Leveraging machine learning to validate the relevance and exclusion restriction assumptions of IV.
• Introducing the concept of 'prediction validity' to check error term independence from the instrument.
• Developing one-stage and two-stage IV approaches based on prediction validity.

Main Results:

• The proposed framework offers empirical validation for instrumental variable assumptions.
• Prediction validity effectively assesses the quality of instruments by testing error term independence.
• Demonstrated performance on a climate change policy-relevant example.

Conclusions:

• Machine learning can significantly enhance the validation of instrumental variable assumptions.
• The prediction validity approach improves the rigor and trustworthiness of causal inference.
• This framework offers a data-driven method for assessing instrument quality in practice.