Core concepts in pharmacoepidemiology: Violations of the positivity assumption in the causal analysis of observational data: Consequences and statistical approaches
Sign Up to like & getrecommendations! Published in 2021 at "Pharmacoepidemiology and Drug Safety"
DOI: 10.1002/pds.5338
Abstract: In the causal analysis of observational data, the positivity assumption requires that all treatments of interest be observed in every patient subgroup. Violations of this assumption are indicated by nonoverlap in the data in the… read more here.
Keywords: assumption; analysis observational; positivity; causal analysis ... See more keywords
Predictability of tropical rainfall and waves: Estimates from observational data
Sign Up to like & getrecommendations! Published in 2020 at "Quarterly Journal of the Royal Meteorological Society"
DOI: 10.1002/qj.3759
Abstract: Funding information Submitted on July 28, 2019 Revised onDecember 27, 2019 For tropical rainfall, there are several potential sources of predictability, including synoptic-scale convectively coupled equatorial waves (CCEWs) and intraseasonal oscillations such as theMadden–Julian Oscillation… read more here.
Keywords: predictability; tropical rainfall; estimated days; predictability tropical ... See more keywords
Extensive studies of the neutron star equation of state from the deep learning inference with the observational data augmentation
Sign Up to like & getrecommendations! Published in 2021 at "Journal of High Energy Physics"
DOI: 10.1007/jhep03(2021)273
Abstract: We discuss deep learning inference for the neutron star equation of state (EoS) using the real observational data of the mass and the radius. We make a quantitative comparison between the conventional polynomial regression and… read more here.
Keywords: inference; observational data; augmentation; data augmentation ... See more keywords
Successful structure learning from observational data
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DOI: 10.1016/j.cognition.2018.06.003
Abstract: Previous work suggests that humans find it difficult to learn the structure of causal systems given observational data alone. We identify two conditions that enable successful structure learning from observational data: people succeed if the… read more here.
Keywords: structure learning; structure; learning observational; successful structure ... See more keywords
Using observational data for personalized medicine when clinical trial evidence is limited.
Sign Up to like & getrecommendations! Published in 2018 at "Fertility and sterility"
DOI: 10.1016/j.fertnstert.2018.04.005
Abstract: Randomized clinical trials are considered the preferred approach for comparing the effects of treatments, yet data from high-quality clinical trials are often unavailable and many clinical decisions are made on the basis of evidence from… read more here.
Keywords: medicine; clinical trial; trial; observational data ... See more keywords
Implementing structural equation models to observational data from feedlot production systems.
Sign Up to like & getrecommendations! Published in 2017 at "Preventive veterinary medicine"
DOI: 10.1016/j.prevetmed.2017.09.002
Abstract: The objective of this study was to illustrate the implementation of a mixed-model-based structural equation modeling (SEM) approach to observational data in the context of feedlot production systems. Different from traditional multiple-trait models, SEMs allow… read more here.
Keywords: production systems; structural equation; effect; feedlot production ... See more keywords
Reconstruction of plant–pollinator networks from observational data
Sign Up to like & getrecommendations! Published in 2021 at "Nature Communications"
DOI: 10.1038/s41467-021-24149-x
Abstract: Empirical measurements of ecological networks such as food webs and mutualistic networks are often rich in structure but also noisy and error-prone, particularly for rare species for which observations are sparse. Focusing on the case… read more here.
Keywords: reconstruction plant; plant pollinator; observational data; pollinator networks ... See more keywords
Direction Dependence Analysis in the Presence of Confounders: Applications to Linear Mediation Models Using Observational Data
Sign Up to like & getrecommendations! Published in 2019 at "Multivariate Behavioral Research"
DOI: 10.1080/00273171.2018.1528542
Abstract: Abstract Statistical methods to identify mis-specifications of linear regression models with respect to the direction of dependence (i.e. whether or better approximates the data-generating mechanism) have received considerable attention. Direction dependence analysis (DDA) constitutes such… read more here.
Keywords: direction; dependence analysis; school; direction dependence ... See more keywords
Targeted Maximum Likelihood Estimation for Causal Inference With Observational Data-The Example of Private Tutoring.
Sign Up to like & getrecommendations! Published in 2025 at "Multivariate behavioral research"
DOI: 10.1080/00273171.2025.2561942
Abstract: State-of-the-art causal inference methods for observational data promise to relax assumptions threatening valid causal inference. Targeted maximum likelihood estimation (TMLE), for example, is a template for constructing doubly robust, semiparametric, efficient substitution estimators, providing consistent… read more here.
Keywords: causal inference; targeted maximum; mathematics; observational data ... See more keywords
Personalized treatment selection using observational data
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Applied Statistics"
DOI: 10.1080/02664763.2021.2019689
Abstract: Estimating the optimal treatment regime based on individual patient characteristics has been a topic of discussion in many forums. Advanced computational power has added momentum to this discussion over the last two decades and practitioners… read more here.
Keywords: personalized treatment; optimal treatment; observational data; treatment selection ... See more keywords
Bayesian doubly robust estimation of causal effects for clustered observational data
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Applied Statistics"
DOI: 10.1080/02664763.2024.2449396
Abstract: Observational data often exhibit clustered structure, which leads to inaccurate estimates of exposure effect if such structure is ignored. To overcome the challenges of modelling the complex confounder effects in clustered data, we propose a… read more here.
Keywords: effects clustered; bayesian doubly; observational data; doubly robust ... See more keywords