方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 因果识别（使用do演算）× | 双重差分法 (Diff-in-Diff)× | |
|---|---|---|
| 领域≠ | 因果推断 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2009 | 1994 |
| 提出者≠ | Judea Pearl | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| 类型≠ | Causal identification framework | Causal inference / panel regression |
| 开创性文献≠ | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| 别名≠ | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| 相关 | 5 | 5 |
| 摘要≠ | DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths. | 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. |
| ScholarGate数据集 ↗ |
|
|