方法对比
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| 自适应自然实验× | 自适应实验× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2000s–2010s (systematic application in policy and social science evaluation) | 1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s |
| 提出者≠ | Synthesizes natural experiment tradition (Meyer 1995; Dunning 2012) with adaptive design principles (Wald 1947; Chow & Chang 2008) | Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others |
| 类型≠ | Quasi-experimental adaptive research design | Experimental research design |
| 开创性文献≠ | Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press. ISBN: 978-1107698000 | Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761 |
| 别名 | adaptive quasi-experiment, adaptive exogenous shock design, adaptive as-if randomization, sequential natural experiment | adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization |
| 相关≠ | 4 | 5 |
| 摘要≠ | An adaptive natural experiment combines the causal logic of the natural experiment — exploiting real-world events that assign individuals to conditions in a plausibly exogenous way — with pre-specified adaptive monitoring rules that allow the analytic protocol to be modified based on accumulating data. This hybrid design is used in economics, epidemiology, and policy evaluation when the natural event unfolds over time and interim evidence can legitimately inform decisions about data collection scope, subgroup focus, or analytic strategy without compromising causal validity. | An adaptive experiment is an experimental design in which pre-specified rules allow the protocol to be modified — such as reallocating participants to better-performing arms, stopping early for efficacy or futility, or changing sample size — based on accumulating interim data, while maintaining statistical validity. Adaptive designs are widely used in clinical trials, behavioural economics, and online platform testing to improve efficiency and ethics without sacrificing inferential rigour. |
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