Machine learning in policy evaluation: new tools for causal inference
N Kreif, K DiazOrdaz - arXiv preprint arXiv:1903.00402, 2019 - arxiv.org
While machine learning (ML) methods have received a lot of attention in recent years, these
methods are primarily for prediction. Empirical researchers conducting policy evaluations …
methods are primarily for prediction. Empirical researchers conducting policy evaluations …
Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects
S Schneeweiss - Clinical epidemiology, 2018 - Taylor & Francis
Background Decision makers in health care increasingly rely on nonrandomized database
analyses to assess the effectiveness, safety, and value of medical products. Health care data …
analyses to assess the effectiveness, safety, and value of medical products. Health care data …
Using super learner prediction modeling to improve high-dimensional propensity score estimation
The high-dimensional propensity score is a semiautomated variable selection algorithm that
can supplement expert knowledge to improve confounding control in nonexperimental …
can supplement expert knowledge to improve confounding control in nonexperimental …
[HTML][HTML] Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review
Purpose The targeted maximum likelihood estimation (TMLE) statistical data analysis
framework integrates machine learning, statistical theory, and statistical inference to provide …
framework integrates machine learning, statistical theory, and statistical inference to provide …
High‐dimensional propensity scores for empirical covariate selection in secondary database studies: planning, implementation, and reporting
JA Rassen, P Blin, S Kloss… - … and Drug Safety, 2023 - Wiley Online Library
Real‐world evidence used for regulatory, payer, and clinical decision‐making requires
principled epidemiology in design and analysis, applying methods to minimize confounding …
principled epidemiology in design and analysis, applying methods to minimize confounding …
[HTML][HTML] Analyses of child cardiometabolic phenotype following assisted reproductive technologies using a pragmatic trial emulation approach
Assisted reproductive technologies (ART) are increasingly used, however little is known
about the long-term health of ART-conceived offspring. Weak selection of comparison …
about the long-term health of ART-conceived offspring. Weak selection of comparison …
When can nonrandomized studies support valid inference regarding effectiveness or safety of new medical treatments?
The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects
of medications. Limitations of RCTs have led to increasing interest in using real‐world …
of medications. Limitations of RCTs have led to increasing interest in using real‐world …
Selective machine learning of doubly robust functionals
Y Cui, EJ Tchetgen Tchetgen - Biometrika, 2024 - academic.oup.com
While model selection is a well-studied topic in parametric and nonparametric regression or
density estimation, selection of possibly high-dimensional nuisance parameters in …
density estimation, selection of possibly high-dimensional nuisance parameters in …
Machine learning for improving high‐dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature
Purpose Supplementing investigator‐specified variables with large numbers of empirically
identified features that collectively serve as 'proxies' for unspecified or unmeasured factors …
identified features that collectively serve as 'proxies' for unspecified or unmeasured factors …
Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data
Propensity score-based estimators are increasingly used for causal inference in
observational studies. However, model selection for propensity score estimation in high …
observational studies. However, model selection for propensity score estimation in high …