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Dynamic Inverse Probability Weighting/Evidence
Method evidence record

Dynamic Inverse Probability Weighting

Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal settings, past treatment affects future covariates, which in turn affect future treatment — a feedback loop that standard regression cannot untangle.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Dynamic Inverse Probability Weighting for Time-Varying Treatments
Taxonomic method record · regression-model / causal-inference
  • 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
  • Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC. · URL
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyDoubly Robust Estimationmachine-suggested · Relational suggestion, not evidence.Same method familyInverse Probability Weightingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMarginal Structural Modelmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketPropensity Score Weightingmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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