পদ্ধতির তুলনা করুন

	কার্যকারণ আবিষ্কার অ্যালগরিদম (PC, FCI, LiNGAM)	কারণগত অনুমানের জন্য ইনস্ট্রুমেন্টাল ভ্যারিয়েবলস (IV) পদ্ধতি
ক্ষেত্র	কার্যকারণ অনুমান	স্বাস্থ্য অর্থনীতি
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	2000	1990s (modern applications)
প্রবর্তক	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Angrist & Pischke (applied econometrics); rooted in econometric theory
ধরন	Causal structure learning	Method
মৌলিক উৎস	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
অপর নাম	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	IV, two-stage least squares, TSLS, causal estimation
সম্পর্কিত	5	3
সারসংক্ষেপ	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.	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.