方法对比

	Factorial Natural Experiment	析因随机对照试验
领域	实验设计	实验设计
方法族	Process / pipeline	Process / pipeline
起源年份	1920s (factorial origins, Fisher); natural experiment formalization 1990s–2000s; factorial natural experiment usage widespread 2000s–present	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
提出者	Extension of natural experiment tradition (Dunning, Angrist & Pischke) combined with factorial design logic (Fisher)	R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)
类型	Quasi-experimental research design	Experimental trial 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	Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504.
别名	factorial quasi-experiment, multi-factor natural experiment, factorial exogenous variation design	Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization
相关	4	6
摘要	A factorial natural experiment exploits naturally occurring exogenous variation across two or more factors simultaneously, allowing researchers to estimate main effects and interactions without random assignment. Natural events, policy changes, or institutional rules create treatment conditions that approximate a factorial structure, enabling causal inference in observational settings where controlled experimentation is infeasible or unethical.	A factorial randomized controlled trial (factorial RCT) is an experimental design in which participants are randomly assigned to every possible combination of two or more independent factors (treatments or intervention components) simultaneously. This allows researchers to estimate the main effect of each factor and their interactions within a single, efficient trial, rather than running separate experiments for each factor.