Regression modelQuasi-experimental / causal inference

Marginal Structural Model (MSM)

A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.

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Sources

  1. Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI: 10.1097/00001648-200009000-00011
  2. Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC. link

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Referenced by

ScholarGateMarginal Structural Model (Marginal Structural Model with Inverse Probability of Treatment Weighting). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/marginal-structural-model