Marginal Structural Models
Marginal Structural Models is a class of causal statistical models that represent the marginal (population-level) counterfactual mean of an outcome as a function of treatment or exposure history and are typically estimated via inverse-probability weighting to identify causal effects in the presence of time-varying confounding affected by prior exposure.
270
Publications
27.5K
Citations
708
Authors
389
Institutions
Publications per year
2017–2026
77
270
1–5 of 270
708
University of California, Berkeley, United States
26 publications · 2.1K citations
19
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13
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12
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11
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1–5 of 708
389
Harvard University
Cambridge, United States
28 h-index
85 publications
26K citations
University of California, Berkeley
Berkeley, United States
24 h-index
69 publications
8K citations

McGill University
Montreal, Canada
20 h-index
58 publications
3K citations
University of Pennsylvania
Philadelphia, United States
13 h-index
29 publications
1.8K citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
13 h-index
30 publications
1.7K citations

McGill University Health Centre
Montreal, Canada
12 h-index
19 publications
801 citations
Cancer Research And Biostatistics
Seattle, United States
10 h-index
13 publications
2.6K citations

Johns Hopkins University
Baltimore, United States
9 h-index
12 publications
3.6K citations
1–8 of 389
35 publications · 2.2K citations
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AJE · 26 publications · 5K citations
21
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15 publications · 9.5K citations
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16 publications · 1.2K citations
14
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12 publications · 1.2K citations
11
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