Compară metode

	Identificarea cauzală cu grafuri aciclice direcționate (do-calculus)	Difference-in-Differences (Diff-in-Diff)	Metoda Variabilelor Instrumentale (IV) pentru Inferența Cauzală	Regresia prin metoda celor mai mici pătrate ordinare (OLS)
Domeniu	Inferență cauzală	Econometrie	Economia sănătății	Econometrie
Familie	Regression model	Regression model	Process / pipeline	Regression model
Anul apariției	2009	1994	1990s (modern applications)	2019
Autorul original	Judea Pearl	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)	Angrist & Pischke (applied econometrics); rooted in econometric theory	Wooldridge (textbook treatment); classical least squares
Tip	Causal identification framework	Causal inference / panel regression	Method	Linear regression
Sursa seminală	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	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
Denumiri alternative	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	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
Înrudite	5	5	3	5
Rezumat	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.	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).