Developed comprehensive documentation for Propensity Score Methods in the mixpanel/docs repository, focusing on enabling robust causal impact analysis and consistent terminology for data scientists and engineers. The work detailed practical implementation guidance, including the use of logistic regression and feature selection techniques, and addressed high-level considerations for applying propensity score methods. Leveraging skills in data analysis, statistical modeling, and documentation, the developer utilized Markdown and JSON to structure content and update spell-check configurations, ensuring accurate recognition of domain-specific terms. This contribution improved documentation quality, streamlined onboarding for causal inference features, and supported faster, more consistent adoption of advanced analytical methodologies.