Adaptive Scenario Simulation (AdSS) Model for Counterfactual Estimation

This study explores the integration of counterfactual thinking and Systematic Inventive Thinking (SIT) in program evaluation through the development of the Adaptive Scenario Simulation (AdSS) model. Counterfactual reasoning plays a critical role in causal inference by estimating alternative outcomes, yet traditional methods face limitations in addressing selection bias and unobservable conditions. By incorporating SIT principles—such as Subtraction, Division, and Attribute Dependency—this study introduces a structured approach to counterfactual estimation that enhances validity and methodological rigor. Utilizing a qualitative research design, including semi-structured interviews and scenario-based simulations, the study seeks to gather insights from evaluation experts to assess the model’s applicability. Findings suggest that the AdSS model improves counterfactual estimation by systematically generating plausible alternative scenarios, fostering more reliable causal inferences. This research contributes to advancing evaluation methodologies by offering an innovative framework that enhances decision-making, program design, and policy development across various domains, including education, health, economic, and international development.