পদ্ধতির তুলনা করুন

	মেশিন লার্নিং-অগমেন্টেড ইন্সট্রুমেন্টাল ভ্যারিয়েবলস (ML-IV)	ল্যাসো রিগ্রেশন
ক্ষেত্র	কার্যকারণ অনুমান	যন্ত্র শিখন
পরিবার	Regression model	Machine learning
উদ্ভবের বছর	2012-2018	1996
প্রবর্তক	Belloni, Chernozhukov & Hansen; Chernozhukov et al.	Tibshirani, R.
ধরন	Causal inference / semi-parametric estimation	Regularized linear regression (L1 penalty)
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.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
অপর নাম	ML-IV, MLIV, Double/Debiased ML with IV, DML-IV	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
সম্পর্কিত	4	4
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.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.