About: Targeting Learning: Robust Statistics for Reproducible Research

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Targeting Learning: Robust Statistics for Reproducible Research
Creator	Hubbard, Alan Arnold, Benjamin Colford, John Benjamin-Chung, Jade Cai, Weixin Coyle, Jeremy Dayal, Sonali Hejazi, Nima Malenica, Ivana Mertens, Andrew Phillips, Rachael Van Der Laan, Mark
source	ArXiv
abstract	Targeted Learning is a subfield of statistics that unifies advances in causal inference, machine learning and statistical theory to help answer scientifically impactful questions with statistical confidence. Targeted Learning is driven by complex problems in data science and has been implemented in a diversity of real-world scenarios: observational studies with missing treatments and outcomes, personalized interventions, longitudinal settings with time-varying treatment regimes, survival analysis, adaptive randomized trials, mediation analysis, and networks of connected subjects. In contrast to the (mis)application of restrictive modeling strategies that dominate the current practice of statistics, Targeted Learning establishes a principled standard for statistical estimation and inference (i.e., confidence intervals and p-values). This multiply robust approach is accompanied by a guiding roadmap and a burgeoning software ecosystem, both of which provide guidance on the construction of estimators optimized to best answer the motivating question. The roadmap of Targeted Learning emphasizes tailoring statistical procedures so as to minimize their assumptions, carefully grounding them only in the scientific knowledge available. The end result is a framework that honestly reflects the uncertainty in both the background knowledge and the available data in order to draw reliable conclusions from statistical analyses - ultimately enhancing the reproducibility and rigor of scientific findings.
has issue date	2020-06-12(xsd:dateTime)
has license	arxiv
sha1sum (hex)	9a68bcc1f802c4f9473a90cf4b7209b23e5d944d
resource representing a document's title	Targeting Learning: Robust Statistics for Reproducible Research
resource representing a document's body	covid:9a68bcc1f802c4f9473a90cf4b7209b23e5d944d#body_text
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