19 papers · ranked by Valyu relevance
Karimollah Hajian Tilaki
Confounding can be thought of as mixing the effect of exposure on the risk of disease with a third factor which distorts the measure of association such as risk ratio or odds ratio. This bias arises because of complex functional relationship of confounder with both exposure and disease (outcome). In this article, we…
Sander Greenland, James M Robins
In 1986 the International Journal of Epidemiology published "Identifiability, Exchangeability and Epidemiological Confounding". We review the article from the perspective of a quarter century after it was first drafted and relate it to subsequent developments on confounding, ignorability, and collapsibility.
Stefania Boccia, Giuseppe La Torre, Roberto Persiani, Domenico D'Ugo + 2 more
Scientific literature may be biased because of the internal validity of studies being compromised by different forms of measurement error, and/or because of the selective reporting of positive and 'statistically significant' results. While the first source of bias might be prevented, and in some cases corrected to a…
Chittaranjan Andrade
Confounding can be prevented or corrected in at least four different ways: 1. By restriction; for example, if the use of hormone replacement therapy confounds the risk of Alzheimer's disease in a hypothetical study of the efficacy of a vaccine for the condition, the sample for the study can be restricted to women who…
Tyler J. VanderWeele, Ilya Shpitser
The causal inference literature has provided a clear formal definition of confounding expressed in terms of counterfactual independence. The literature has not, however, come to any consensus on a formal definition of a confounder, as it has given priority to the concept of confounding over that of a confounder. We…
John W. Benning, Jedidiah Carlson, Olivia S. Smith, Ruth G. Shaw + 1 more
The scientific literature has seen a resurgence of interest in genetic influences on human behavior and socioeconomic outcomes. Such studies face the central difficulty of distinguishing possible causal influences, in particular genetic and non-genetic ones. When confounding between possible influences is not…
Richard Dinga, Lianne Schmaal, Brenda W.J.H. Penninx, Dick J. Veltman + 1 more
Machine learning predictive models are being used in neuroimaging to predict information about the task or stimuli or to identify potentially clinically useful biomarkers. However, the predictions can be driven by confounding variables unrelated to the signal of interest, such as scanner effect or head motion, limiting…
Carlo A. Furia, Richard Torkar
Omitted variable bias occurs when a statistical model leaves out variables that are relevant determinants of the effects under study. This results in the model attributing the missing variables' effect to some of the included variables—hence over- or under-estimating the latter's true effect. Omitted variable bias…
Etsuji Suzuki, Michio Yamamoto, Eiji Yamamoto
Background The counterfactual definition of confounding is often explained in the context of exchangeability between the exposed and unexposed groups. One recent approach is to examine whether the measures of association (eg, associational risk difference) are exchangeable when exposure status is flipped in the…
Mimi Liljeholm
As scientists, we are keenly aware that if putative causes perfectly co-vary, the independent influence of neither can be discerned – a “no confounding” constraint on inference, fundamental to philosophical and statistical perspectives on causation. Intriguingly, a substantial behavioral literature suggests that naïve…
Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
Detecting and measuring confounding effects from data is a key challenge in causal inference. Existing methods frequently assume causal sufficiency, disregarding the presence of unobserved confounding variables. Causal sufficiency is both unrealistic and empirically untestable. Additionally, existing methods make…
Elias Chaibub Neto
Clinical machine learning applications are often plagued with confounders that are clinically irrelevant, but can still artificially boost the predictive performance of the algorithms. Confounding is especially problematic in mobile health studies run "in the wild", where it is challenging to balance the demographic…
Mahsa Monshizadeh, Yuhui Hong, Yuzhen Ye
As in many fields, the presence of confounding effects (or biases) presents a significant challenge in micro-biome research, including using microbiome data to predict host phenotypes. If not properly addressed, confounders can lead to spurious associations, biased predictions and misleading interpretations. One…
Zijie Zhao, Xiaoyu Yang, Jiacheng Miao, Stephen Dorn + 3 more
Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly…
Pierre J. C. Chuard, Milan Vrtílek, Megan L. Head, Michael D. Jennions
There is increased concern about poor scientific practices arising from an excessive focus on P-values. Two particularly worrisome practices are selective reporting of significant results and ‘P-hacking’. The latter is the manipulation of data collection, usage, or analyses to obtain statistically significant outcomes.…
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
Judea Pearl
This note deals with a class of variables that, if conditioned on, tends to amplify confounding bias in the analysis of causal effects. This class, independently discovered by Bhattacharya and Vogt (2007) and Wooldridge (2009), includes instrumental variables and variables that have greater influence on treatment…
Xinran Li
Designs Authors: ['Xinran Li'] Observational studies provide invaluable opportunities to draw causal inference, but they may suffer from biases due to pretreatment difference between treated and control units. Matching is a popular approach to reduce observed covariate imbalance. To tackle unmeasured confounding, a…
Authors not listed
The precision of thermodynamic modeling for ionic liquid (IL)–solute systems is fundamentally reliant on the quality of experimental data. However, prevalent databases such as ILThermo frequently exhibit conflicting measurements for the same systems under identical temperature and pressure conditions. These disparities…