Dynamic Marginal Structural Models
Dynamic Marginal Structural Models is a causal inference framework that extends marginal structural models to estimate the effects of time-varying treatments by accounting for time-dependent confounders through the specification of dynamic treatment regimes, thereby enabling the evaluation of policies in which treatment decisions depend adaptively on the evolving history of covariates and prior treatments.
24
Publications
1.6K
Citations
94
Authors
70
Institutions
Publications per year
2017–2026
7
Parents
24
Bibhas Chakraborty, Susan A. Murphy · Annual Review of Statistics and Its Application · 2013 · 296 citations · Full text
Liliana Orellana, Andrea Rotnitzky, James M. Robins · The International Journal of Biostatistics · 2010 · 206 citations · Full text
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Harvard University
Cambridge, United States
7 h-index
9 publications
682 citations
University of California, Berkeley
Berkeley, United States
3 h-index
8 publications
822 citations
Universidad Torcuato Di Tella
Buenos Aires, Argentina
3 h-index
3 publications
270 citations
Argentine Antarctic Institute
Buenos Aires, Argentina
2 h-index
2 publications
258 citations
Universidad de Buenos Aires
Buenos Aires, Argentina
2 h-index
2 publications
258 citations
Kaiser Permanente
Oakland, United States
2 h-index
3 publications
171 citations

McGill University
Montreal, Canada
2 h-index
5 publications
130 citations

University of Maryland, Baltimore
Baltimore, United States
2 h-index
4 publications
286 citations
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3 publications · 123 citations
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3 publications · 67 citations
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2 publications · 258 citations
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AJE · 1 publications · 18 citations
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1 publications · 14 citations
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