Salīdzināt metodes

	Dinamiskā entropijas balansēšana	Dinamiskā tendences rādītāja saskaņošana
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2012-2018	1986-2010
Autors	Hainmueller (2012) for static entropy balancing; extended to dynamic settings by Blackwell and Glynn (2018) and subsequent methodologists	Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching
Tips	Causal inference / weighting estimator	Sequential causal matching
Pirmavots	Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46.	Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.
Citi nosaukumi	DEB, longitudinal entropy balancing, entropy balancing with time-varying treatment, sequential entropy balancing	dynamic PSM, sequential propensity score matching, longitudinal propensity matching, DPSM
Saistītās	6	6
Kopsavilkums	Dynamic Entropy Balancing extends the entropy balancing reweighting approach to settings with time-varying treatments in panel or longitudinal data. It constructs unit weights at each time period such that the covariate distributions of treated and comparison units are balanced on specified moments, adjusting sequentially for prior treatment history and time-varying confounders to estimate the causal effect of treatment sequences on outcomes.	Dynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire treatment sequences or regime changes by constructing matched comparisons at each decision point using the full history of covariates and prior treatments.