方法对比

	Factorial Natural Experiment	双重差分法 (Diff-in-Diff)
领域	实验设计	计量经济学
方法族	Process / pipeline	Regression model
起源年份	1920s (factorial origins, Fisher); natural experiment formalization 1990s–2000s; factorial natural experiment usage widespread 2000s–present	1994
提出者	Extension of natural experiment tradition (Dunning, Angrist & Pischke) combined with factorial design logic (Fisher)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
类型	Quasi-experimental research design	Causal inference / panel regression
开创性文献	Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press. ISBN: 978-1107698000	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
别名	factorial quasi-experiment, multi-factor natural experiment, factorial exogenous variation design	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
相关	4	5
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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.