Introduction to the Special Issue on Propensity Score Methods in Behavioral Research

Area of Science:

• Epidemiology
• Biostatistics
• Causal Inference

Background:

• Observational data analysis is crucial for understanding causal relationships.
• Propensity score (PS) methods are widely used for causal inference in observational studies.
• Existing methods face challenges in complex data structures and application contexts.

Purpose of the Study:

• To present a collection of articles on propensity score (PS) methods for causal analyses of observational data.
• To offer insights into the logic, methods, and models underpinning PS analysis.
• To provide recommendations for advancing the application of PS methods in future research.

Main Methods:

• The issue covers a general introduction to propensity score analysis (PSA).
• It details the use of PSA in mediation studies and covariate selection.
• Hierarchical data issues, models, and specific applications are discussed.

Main Results:

• The articles collectively demonstrate the utility of PS methods across various scenarios.
• Challenges and nuances in applying PSA are highlighted.
• An educational testing context example illustrates practical application.

Conclusions:

• Propensity score (PS) methods offer a robust framework for causal inference from observational data.
• Future research should consider stronger randomization designs and graphical methods.
• Revisiting the definition of "treatments" and prioritizing prospective designs can enhance causal analyses.