Causal criteria: time has come for a revision

2019 ◽  
Vol 34 (6) ◽  
pp. 537-541 ◽  
Author(s):  
Jørn Olsen ◽  
Uffe Juul Jensen
Keyword(s):  
Causal Criteria

Identifying critical causal criteria of green supplier evaluation using heterogeneous judgements: An integrated approach based on cloud model and DEMATEL

2021 ◽  
Vol 113 ◽  
pp. 107882
Author(s):  
Hengxia Gao ◽  
Yanbing Ju ◽  
Ernesto D.R. Santibanez Gonzalez ◽  
Xiao-Jun Zeng ◽  
Peiwu Dong ◽  
...  
Keyword(s):  
Cloud Model ◽  
Integrated Approach ◽  
Supplier Evaluation ◽  
Causal Criteria

Inference 2: Causation

2020 ◽  
pp. 271-284
Author(s):  
Robert Stewart
Keyword(s):  
Seventeenth Century ◽  
Life Sciences ◽  
Modern Science ◽  
Descriptive Study ◽  
Effect Modification ◽  
Mediation Effect ◽  
Western Philosophy ◽  
Experimental Conditions ◽  
Bradford Hill ◽  
Causal Criteria

Most epidemiological research, beyond the simple descriptive study, is attempting to elucidate a causal relationship. This chapter continues the consideration of causal inference in a broader context, covering the principles of inductivism and refutationism that emerged in seventeenth-century Western philosophy and which have had profound influences on modern science. However, life sciences cannot rely on perfectly controlled experimental conditions and consequently a number of other principles have had to be developed to allow knowledge to accumulate despite uncertainties in hypothesis testing. For epidemiology, these include the principle of consensus (repeated experiments contributing to a ‘verdict of causality’) and the causal criteria outlined by Bradford Hill. Finally, the potential combinations of variables under investigation (causal, confounding, mediation, effect modification) are considered in relation to analysis designs.

On the Origin of Hillʼs Causal Criteria

Epidemiology ◽  
1991 ◽  
Vol 2 (5) ◽  
pp. 367-369 ◽  
Author(s):  
Alfredo Morabia
Keyword(s):  
Causal Criteria

scholarly journals Non-specific blood tests as proxies for COVID-19 hospitalisation: are there plausible associations after excluding noisy predictors?

2021 ◽  
Vol 149 ◽  
Author(s):  
G. Ishikawa ◽  
G. Argenti ◽  
C. B. Fadel
Keyword(s):  
Risk Factor ◽  
Area Under The Curve ◽  
Albert Einstein ◽  
Directed Acyclic Graphs ◽  
Rt Pcr ◽  
Reactive Protein ◽  
Blood Tests ◽  
Acyclic Graphs ◽  
Causal Criteria ◽  
The Difference

Abstract This study applied causal criteria in directed acyclic graphs for handling covariates in associations for prognosis of severe coronavirus disease 2019 (COVID-19) cases. To identify non-specific blood tests and risk factors as predictors of hospitalisation due to COVID-19, one has to exclude noisy predictors by comparing the concordance statistics (area under the curve − AUC) for positive and negative cases of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Predictors with significant AUC at negative stratum should be either controlled for their confounders or eliminated (when confounders are unavailable). Models were classified according to the difference of AUC between strata. The framework was applied to an open database with 5644 patients from Hospital Israelita Albert Einstein in Brazil with SARS-CoV-2 reverse transcription – polymerase chain reaction (RT-PCR) exam. C-reactive protein (CRP) was a noisy predictor: hospitalisation could have happened due to causes other than COVID-19 even when SARS-CoV-2 RT-PCR is positive and CRP is reactive, as most cases are asymptomatic to mild. Candidates of characteristic response from moderate-to-severe inflammation of COVID-19 were: combinations of eosinophils, monocytes and neutrophils, with age as risk factor; and creatinine, as risk factor, sharpens the odds ratio of the model with monocytes, neutrophils and age.

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