Research on Measuring User Behavior Response Differences Supported by Propensity Scoring Method

Measuring the differences in user behavior responses is increasingly crucial for identifying targeted intervention effects and improving overall digital platform operation in the era of big data. However, observational studies often face significant methodological challenges. To overcome the profound influence of user characteristic differences, inherent sample selection bias, and unobserved confounding factors, the propensity score method is systematically utilized to construct a robust measurement system. This study meticulously classifies users based on the specific type of behavior response and deeply explores the empirical possibility of applying this advanced statistical approach in complex groups that were not randomly sampled. A comprehensive and complete model is constructed around critical methodological issues, including the precise selection of dependent variables, the rigorous elimination of covariates, the accurate estimation of propensity scores, and the implementation of advanced sample matching and weighting techniques. Furthermore, analytical methods such as inter-group comparison, heterogeneity testing, and extensive robustness testing are adopted to significantly enhance the accuracy, reliability, and persuasiveness of the measurement results. Ultimately, this research provides vital technical support and actionable insights for platform administrators and marketers in identifying nuanced behavioral characteristics, rigorously evaluating marketing intervention effects, and strategically optimizing enterprise operations for sustainable growth and improved user engagement.