Linganisha mbinu

	Kikokotozi cha Kulinganisha Vipindi Vingi	Kikokotozi cha Kulinganisha chenye Nguvu
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2005	2010
Mwanzilishi	Abadie (2005); Imbens & Wooldridge (2009)	Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)
Aina	Quasi-experimental / causal inference	Nonparametric causal inference / matching
Chanzo asilia	Abadie, A. (2005). Semiparametric Difference-in-Differences Estimators. Review of Economic Studies, 72(1), 1-19.	Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.
Majina mbadala	panel matching estimator, longitudinal matching, multi-wave matching, repeated-cross-section matching	dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME
Zinazohusiana	6	6
Muhtasari	The multi-period matching estimator extends the standard matching framework to settings with multiple time periods, pairing each treated unit to similar untreated units based on pre-treatment covariates or propensity scores, then using within-pair before-after differences to estimate the average treatment effect on the treated (ATT). Leveraging repeated observations, it simultaneously controls for observed confounders and time-invariant unobserved heterogeneity.	The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions.