The field of nutritional epidemiology faces challenges posed by measurement error, diet as
a complex exposure, and residual confounding. The objective of this perspective article is to …

Abstract Machine learning is a branch of computer science that has the potential to transform
epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new …

This book builds on and is a sequel to our book Targeted Learning: Causal Inference for
Observational and Experimental Studies (2011). Since the publication of this first book on …

Abstract Purpose of Review Propensity score methods have become commonplace in
pharmacoepidemiology over the past decade. Their adoption has confronted formidable …

Covariate adjustment is a commonly used method for total causal effect estimation. In recent
years, graphical criteria have been developed to identify all valid adjustment sets, that is, all …

Accurate estimating causal effects of crashes on highway traffic is crucial for mitigating the
negative impacts of crashes. Previous studies have built up a series of methods via …

Controlling for confounding bias is crucial in causal inference. Distinct methods are currently
employed to mitigate the effects of confounding bias. Each requires the introduction of a set …

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

Randomized controlled trials (RCTs) may suffer from limited scope. In particular, samples
may be unrepresentative: some RCTs over-or under-sample individuals with certain …

To unbiasedly estimate a causal effect on an outcome unconfoundedness is often assumed.
If there is sufficient knowledge on the underlying causal structure then existing confounder …