Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Maya L Petersen

Showing results (1-10 of 134) with videos related to

Pageof 14
Sort By:
Epidemiology (Cambridge, Mass.)|September 30, 2014
Commentary: Applying a causal road map in settings with time-dependent confoundingMaya L Petersen
Epidemiology (Cambridge, Mass.)|April 6, 2011
Compound treatments, transportability, and the structural causal model: the power and simplicity of causal graphsMaya L Petersen
American Journal of Epidemiology|March 22, 2021
Invited Commentary: Machine Learning in Causal Inference-How Do I Love Thee? Let Me Count the WaysLaura B Balzer, Maya L Petersen
The Lancet. HIV|October 2, 2015
Network meta-analyses: powerful but not without perilsElvin H Geng, Maya L Petersen
The International Journal of Biostatistics|April 3, 2012
Direct effect modelsMark J van der Laan, Maya L Petersen
The International Journal of Biostatistics|January 6, 2009
Causal effect models for realistic individualized treatment and intention to treat rulesMark J van der Laan, Maya L Petersen
American Journal of Epidemiology|March 15, 2011
Petersen et al. Respond to "Effect Modification by Time-varying Covariates"Maya L Petersen, Mark J van der Laan
The International Journal of Biostatistics|January 6, 2009
Statistical learning of origin-specific statically optimal individualized treatment rulesMark J van der Laan, Maya L Petersen
Epidemiology (Cambridge, Mass.)|April 10, 2014
Causal models and learning from data: integrating causal modeling and statistical estimationMaya L Petersen, Mark J van der Laan
American Journal of Epidemiology|December 18, 2014
Improving propensity score estimators' robustness to model misspecification using super learnerRomain Pirracchio, Maya L Petersen, Mark van der Laan
Pageof 14

Showing results (1-10 of 134) with videos related to

Sort By:
Pageof 14
Epidemiology (Cambridge, Mass.)|September 30, 2014
Commentary: Applying a causal road map in settings with time-dependent confoundingMaya L Petersen
Epidemiology (Cambridge, Mass.)|April 6, 2011
Compound treatments, transportability, and the structural causal model: the power and simplicity of causal graphsMaya L Petersen
American Journal of Epidemiology|March 22, 2021
Invited Commentary: Machine Learning in Causal Inference-How Do I Love Thee? Let Me Count the WaysLaura B Balzer, Maya L Petersen
The Lancet. HIV|October 2, 2015
Network meta-analyses: powerful but not without perilsElvin H Geng, Maya L Petersen
The International Journal of Biostatistics|April 3, 2012
Direct effect modelsMark J van der Laan, Maya L Petersen
The International Journal of Biostatistics|January 6, 2009
Causal effect models for realistic individualized treatment and intention to treat rulesMark J van der Laan, Maya L Petersen
American Journal of Epidemiology|March 15, 2011
Petersen et al. Respond to "Effect Modification by Time-varying Covariates"Maya L Petersen, Mark J van der Laan
The International Journal of Biostatistics|January 6, 2009
Statistical learning of origin-specific statically optimal individualized treatment rulesMark J van der Laan, Maya L Petersen
Epidemiology (Cambridge, Mass.)|April 10, 2014
Causal models and learning from data: integrating causal modeling and statistical estimationMaya L Petersen, Mark J van der Laan
American Journal of Epidemiology|December 18, 2014
Improving propensity score estimators' robustness to model misspecification using super learnerRomain Pirracchio, Maya L Petersen, Mark van der Laan
Pageof 14