Regression-Based Causal Analysis from the Potential Outcomes Perspective.

Area of Science:

• Econometrics
• Causal Inference
• Statistical Modeling

Background:

• Empirical economic research frequently uses regression methods to assess causal relationships.
• Assessing causal effects requires understanding counterfactuals.
• Existing regression protocols may not fully account for causal identification conditions.

Purpose of the Study:

• To present a comprehensive regression-based potential outcomes framework for causal modeling, estimation, and inference.
• To clarify the causal interpretability of effect parameters within this framework.
• To identify the conditions necessary for parameter identification.

Main Methods:

• Development of a potential outcomes framework integrated with regression analysis.
• Specification of effect parameters and their causal interpretation.
• Analysis of identification conditions for regression and effect parameters.

Main Results:

• The proposed framework rigorously defines effect parameters and their causal interpretability.
• It clearly outlines the conditions for identifying both effect and regression parameters.
• The framework demonstrates advantages over conventional methods in handling omitted variable bias.

Conclusions:

• The potential outcomes framework offers a more robust approach to causal inference in regression analysis.
• It enhances the understanding and application of causal modeling in empirical economics.
• This framework resolves fundamental issues in conventional approaches to omitted variable bias.