Compară metode

	Analiză de sensibilitate augmentată prin învățare automată pentru cauzalitate	Designul de discontinuitate a regresiei (RDD)
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	2018-2020	2008
Autorul original	Cinelli & Hazlett (sensitivity framework); Chernozhukov et al. (ML augmentation for causal estimation)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Tip	Sensitivity analysis / causal robustness assessment	Quasi-experimental causal design
Sursa seminală	Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: extending omitted variable bias. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39-67.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Denumiri alternative	ML-augmented sensitivity analysis, ML sensitivity analysis for causality, machine learning sensitivity analysis, debiased ML sensitivity analysis	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Înrudite	5	5
Rezumat	Machine learning-augmented sensitivity analysis combines flexible ML estimators with formal robustness checks to assess how much unmeasured confounding would be required to overturn a causal finding. Rooted in Chernozhukov et al.'s double/debiased ML framework and Cinelli and Hazlett's omitted-variable-bias sensitivity tools, it delivers both high-dimensional covariate adjustment and transparent communication of remaining uncertainty about unobserved confounders.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.