方法对比

	动态熵平衡	倾向得分加权法 (PSW / IPW)
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2012-2018	1983 (propensity score); 2003 (efficient IPW estimator)
提出者	Hainmueller (2012) for static entropy balancing; extended to dynamic settings by Blackwell and Glynn (2018) and subsequent methodologists	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
类型	Causal inference / weighting estimator	Causal inference / reweighting
开创性文献	Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46.	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.
别名	DEB, longitudinal entropy balancing, entropy balancing with time-varying treatment, sequential entropy balancing	PSW, inverse probability weighting, IPW, propensity-based weighting
相关	6	6
摘要	Dynamic Entropy Balancing extends the entropy balancing reweighting approach to settings with time-varying treatments in panel or longitudinal data. It constructs unit weights at each time period such that the covariate distributions of treated and comparison units are balanced on specified moments, adjusting sequentially for prior treatment history and time-varying confounders to estimate the causal effect of treatment sequences on outcomes.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).