方法对比

	教育研究中的倾向得分匹配	粗化精确匹配 (CEM)
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	1983 (foundational); education adoption widespread from late 1990s	2011-2012
提出者	Rosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002)	Iacus, King, & Porro
类型	Quasi-experimental / matching-based causal inference	Matching / causal inference
开创性文献	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
别名	PSM in education, educational PSM, PSM for program evaluation in schools, propensity matching education	CEM, coarsened matching, monotonic imbalance bounding matching
相关	5	6
摘要	Propensity Score Matching (PSM) in education research is a quasi-experimental technique that creates comparable treatment and control groups from observational student, teacher, or school data. By balancing groups on observed background characteristics, it enables credible causal estimates of educational interventions — such as tutoring programs, school choice policies, or teacher professional development — when random assignment is infeasible.	Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.