विधियों की तुलना करें

	मशीन लर्निंग-ऑग्मेंटेड सिंथेटिक कंट्रोल मेथड	कार्यकारण प्रभाव विश्लेषण
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	2021	2015
प्रवर्तक	Ben-Michael, Feller & Rothstein	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
प्रकार	Causal inference / quasi-experimental	Bayesian causal inference / counterfactual forecasting
मौलिक स्रोत	Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The augmented synthetic control method. Journal of the American Statistical Association, 116(536), 1789-1803.	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.
उपनाम	ML-augmented SCM, augmented synthetic control, ASC, penalized synthetic control	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
संबंधित	5	5
सारांश	The machine learning-augmented synthetic control method extends the classical synthetic control estimator by using penalized regression or other ML algorithms — such as lasso, ridge, or random forests — to construct the donor weights and to model pre-treatment outcome trajectories. The augmentation corrects for residual imbalance left by the standard weighting step, yielding lower bias when no perfect synthetic control exists.	Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals.