Порівняння методів

	Різниця різниць (Diff-in-Diff)	Метод інструментальних змінних (ІЗ) для причинно-наслідкового висновку	Регресія звичайно найменших квадратів (ЗНК)	Модель фіксованих ефектів панельних даних
Галузь	Економетрика	Економіка охорони здоров'я	Економетрика	Економетрика
Родина	Regression model	Process / pipeline	Regression model	Regression model
Рік появи	1994	1990s (modern applications)	2019	2014
Автор методу	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)	Angrist & Pischke (applied econometrics); rooted in econometric theory	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
Тип	Causal inference / panel regression	Method	Linear regression	Panel data regression
Основоположне джерело	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Інші назви	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)	IV, two-stage least squares, TSLS, causal estimation	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Пов'язані	5	3	5	5
Підсумок	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.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).