विधियों की तुलना करें

	DAG Causal Identification	अंतर-में-अंतर (डिफ-इन-डिफ)	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	कारणात्मक अनुमान	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model	Regression model
उद्भव वर्ष	2009	1994	2019
प्रवर्तक	Judea Pearl	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)	Wooldridge (textbook treatment); classical least squares
प्रकार	Causal identification framework	Causal inference / panel regression	Linear regression
मौलिक स्रोत	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	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
उपनाम	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
संबंधित	5	5	5
सारांश	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.	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).