Dynamisk propensity score-matching
Dynamisk propensity score-matching (DPSM) udvider klassisk propensity score-matching til situationer, hvor behandling gentagne gange tildeles over tid, og tidligere behandlingsvalg påvirker senere valg. Den estimerer den kausale effekt af hele behandlingssekvenser eller regimeændringer ved at konstruere matchede sammenligninger ved hvert beslutningspunkt ved hjælp af hele historikken af kovariater og tidligere behandlinger.
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Method map
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Kilder
- Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137. DOI: 10.1007/s00181-009-0297-3 ↗
- Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512. DOI: 10.1016/0270-0255(86)90088-6 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Dynamic Propensity Score Matching for Sequential Treatments. ScholarGate. https://scholargate.app/da/causal-inference/dynamic-propensity-score-matching
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Dobbelt Robust Estimation (AIPW)Kausal inferens↔ compare
- Dynamisk Difference-in-DifferencesKausal inferens↔ compare
- Vægtning med den inverse behandlingssandsynlighed (IPW / IPTW)Kausal inferens↔ compare
- Marginal Structural Model (MSM)Kausal inferens↔ compare
- Propensity Score MatchingForskningsstatistik↔ compare
- Propensity Score Weighting (PSW / IPW)Kausal inferens↔ compare
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