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 …

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 …

The high-dimensional propensity score is a semiautomated variable selection algorithm that
can supplement expert knowledge to improve confounding control in nonexperimental …

Purpose The targeted maximum likelihood estimation (TMLE) statistical data analysis
framework integrates machine learning, statistical theory, and statistical inference to provide …

Real‐world evidence used for regulatory, payer, and clinical decision‐making requires
principled epidemiology in design and analysis, applying methods to minimize confounding …

Assisted reproductive technologies (ART) are increasingly used, however little is known
about the long-term health of ART-conceived offspring. Weak selection of comparison …

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 …

While model selection is a well-studied topic in parametric and nonparametric regression or
density estimation, selection of possibly high-dimensional nuisance parameters in …

Purpose Supplementing investigator‐specified variables with large numbers of empirically
identified features that collectively serve as 'proxies' for unspecified or unmeasured factors …

Propensity score-based estimators are increasingly used for causal inference in
observational studies. However, model selection for propensity score estimation in high …