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OblastKauzalno zaključivanjeKauzalno zaključivanje
PorodicaRegression modelRegression model
Godina nastanka1986-20102021
TvoracRobins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matchingCallaway & Sant'Anna; Sun & Abraham
TipSequential causal matchingCausal inference / quasi-experimental
Temeljni izvorLechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137. DOI ↗Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗
Drugi nazividynamic PSM, sequential propensity score matching, longitudinal propensity matching, DPSMDynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
Srodne64
SažetakDynamic 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.Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time.
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ScholarGateUporedite metode: Dynamic Propensity Score Matching · Dynamic Difference-in-Differences. Preuzeto 2026-06-17 sa https://scholargate.app/sr/compare