Jämför metoder

	Simuleringsassisterad kausal-komparativ forskning	Propensity score-matchning
Ämnesområde	Forskningsdesign	Forskningsstatistik
Familj	Process / pipeline	Process / pipeline
Ursprungsår	Late 20th–early 21st century (hybrid approach formalized ~1990s–2000s)	1983
Upphovsperson	Synthesized from causal-comparative tradition (Donald T. Campbell; Julian Stanley) and simulation methodology	Paul Rosenbaum and Donald Rubin
Typ	Hybrid observational-simulation design	Method
Ursprungskälla	Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill. ISBN: 978-1260087352	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Alias	simulation-augmented causal-comparative design, ex post facto simulation design, SA-CCR, causal-comparative with simulation validation	PSM, propensity score weighting, covariate balance
Närliggande	4	3
Sammanfattning	Simulation-assisted causal-comparative research is a hybrid observational design that combines the ex post facto logic of causal-comparative studies — comparing groups that differ on a naturally occurring variable — with computational simulation to strengthen causal inference, test counterfactuals, and assess the robustness of observed group differences. By augmenting real-world comparisons with simulated scenarios, researchers can explore causal mechanisms that cannot be manipulated experimentally.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.