পদ্ধতির তুলনা করুন

	মেশিন লার্নিং-অগমেন্টেড ইন্সট্রুমেন্টাল ভ্যারিয়েবলস (ML-IV)	কারণগত অনুমানের জন্য ইনস্ট্রুমেন্টাল ভ্যারিয়েবলস (IV) পদ্ধতি
ক্ষেত্র	কার্যকারণ অনুমান	স্বাস্থ্য অর্থনীতি
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	2012-2018	1990s (modern applications)
প্রবর্তক	Belloni, Chernozhukov & Hansen; Chernozhukov et al.	Angrist & Pischke (applied econometrics); rooted in econometric theory
ধরন	Causal inference / semi-parametric estimation	Method
মৌলিক উৎস	Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
অপর নাম	ML-IV, MLIV, Double/Debiased ML with IV, DML-IV	IV, two-stage least squares, TSLS, causal estimation
সম্পর্কিত	4	3
সারসংক্ষেপ	Machine learning-augmented instrumental variables combines the causal identification power of classical IV with modern high-dimensional machine learning — using methods such as LASSO, random forests, or neural networks to select valid instruments and model nuisance functions, thereby improving first-stage fit and enabling valid inference even when the number of potential instruments or controls is large relative to the sample size.	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.