Porovnat metody

	Algoritmy pro objevování kauzality (PC, FCI, LiNGAM)	Kauzalní identifikace pomocí orientovaných acyklických grafů (do-calculus)	Rozdíl v rozdílech (Diff-in-Diff)	Regrese metodou ordinárních nejmenších čtverců (OLS)
Obor	Kauzální inference	Kauzální inference	Ekonometrie	Ekonometrie
Rodina	Regression model	Regression model	Regression model	Regression model
Rok vzniku	2000	2009	1994	2019
Tvůrce	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Judea Pearl	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)	Wooldridge (textbook treatment); classical least squares
Typ	Causal structure learning	Causal identification framework	Causal inference / panel regression	Linear regression
Původní zdroj	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	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
Další názvy	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	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
Příbuzné	5	5	5	5
Shrnutí	Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges.	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).