Salīdzināt metodes

	Dinamiskais saskaņošanas novērtētājs	Apgrieztā varbūtības svēršana (IPW / IPTW)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2010	2000
Autors	Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)	Robins, Hernán & Brumback
Tips	Nonparametric causal inference / matching	Causal inference weighting estimator
Pirmavots	Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Citi nosaukumi	dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Saistītās	6	5
Kopsavilkums	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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.