M
Maya L. Petersen
Researcher at University of California, Berkeley
Publications - 239
Citations - 8899
Maya L. Petersen is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 45, co-authored 202 publications receiving 6813 citations. Previous affiliations of Maya L. Petersen include Oswaldo Cruz Foundation & Makerere University.
Papers
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Journal ArticleDOI
Diagnosing and Responding to Violations in the Positivity Assumption
TL;DR: The positivity assumption is discussed in the context of assessing model and parameter-specific identifiability of causal effects and several approaches for improving the identifiable of parameters in the presence of positivity violations are reviewed.
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Estimation of direct causal effects.
TL;DR: An estimation approach for natural direct effects that can be implemented using standard statistical software is presented, and the assumptions underlying the approach are reviewed (which are less restrictive than those proposed by previous authors).
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Promoting Transparency in Social Science Research
Edward Miguel,Colin F. Camerer,Katherine Casey,Joshua Cohen,Kevin M. Esterling,Alan S. Gerber,Rachel Glennerster,Donald P. Green,Macartan Humphreys,Guido W. Imbens,David D. Laitin,Temina Madon,Leif D. Nelson,Brian A. Nosek,Brian A. Nosek,Maya L. Petersen,Richard Sedlmayr,Joseph P. Simmons,Uri Simonsohn,M. J. van der Laan +19 more
TL;DR: There is growing appreciation for the advantages of experimentation in the social sciences, and changes have been particularly pronounced in development economics, where hundreds of randomized trials have been carried out over the last decade.
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Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study
Romain Pirracchio,Romain Pirracchio,Maya L. Petersen,Marco Carone,Matthieu Resche Rigon,Sylvie Chevret,Mark J. van der Laan +6 more
TL;DR: Compared with conventional severity scores, Super Learner offers improved performance for predicting hospital mortality in patients in intensive care units and showed better calibration properties than previous score systems.
Journal ArticleDOI
Retention in Care among HIV-Infected Patients in Resource-Limited Settings: Emerging Insights and New Directions
Elvin Geng,Denis Nash,Andrew Kambugu,Yao Zhang,Paula Braitstein,Katerina A. Christopoulos,Winnie Muyindike,Mwebesa Bwana,Constantin T. Yiannoutsos,Maya L. Petersen,Jeffrey N. Martin +10 more
TL;DR: Research to assess and improve retention in care for HIV-infected patients can be strengthened by incorporating novel methods such as sampling-based approaches and a causal analytic framework.