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/cau·sal/ (kaw´z'l) pertaining to, involving, or indicating a cause.


relating to or emanating from cause.

causal association
a noxious agent is said to have a causal association with a particular disease when it can be shown that it plays some role in producing the occurrence of the disease. Generally both biological information and statistical information are combined to infer causal associations.
causal inference
preliminary diagnosis.
causal modeling
construction of models which set out the various relationships between causal agents and the initiation of a disease.

Patient discussion about causal

Q. how is depression caused by having cancer treated? I mean not only the patient, also the family members who tend to get depressed by the situation. how can you treat thi skind of depression?

A. thanks guys, you are great. Nice to have such a community here.

Q. What causes fibromyalgia? Is fibromyalgia a deadly disease?

A. The causes of fibromyalgia are not known. But there are many theories such as abnormalities in brain chemicals, infections, trauma, genetics and hormonal changes. Factors such as poor sleep, fatigue, overexertion and anxiety, may aggravate the symptoms. Fibromyalgia is not a progressive or life-threatening condition, but it affects quality of life. Fibromyalgia is only a disorder of muscles and not a disease.

Q. Is that true that mouth sores are caused by lack of vitamins? I’ve been having white mouth sores in the past 6 months or so. Could that mean I have to take vitamin supplements?

A. yup ... autoimmune reactions means your immune system is not working well
it's not working well because it lacks the nutrient and vitamins it needs to function properly
- take lots of vitamin c to boost your immune system
- organic multivitamins
- organic juices high in anti oxidants
- and most important .. omega 3-6-9

More discussions about causal
References in periodicals archive ?
Granger causality is not a true causal modeling approach, but a heuristic one that argues that omitted covariates that are correlated with time-varying exposure and outcome are as likely to be correlated with tomorrow's exposure as with yesterday's exposure.
Throughout the book, Morton provides exercises for readers to attempt and discusses the maxims of causal modeling in the context of several disorders, making this work a valuable learning device instead of simply an interesting read.
An ERM system based on causal modeling can identify the relationships (including both positive and negative correlations) among trends and events that could impact the enterprise.
The causal modeling approach examines cause and effect relationships among variables.
The causal modeling technique employed here is essentially a heuristic tool that postulates a model of the causal relationships among a system of variables and then uses statistical analysis to estimate the magnitude of these "causal" impacts, assuming that the model provides an appropriate fit to reality in the first lace.
Even more troubling, correlational analysis and causal modeling contain other fundamental deficiencies.
In recent years, notable advancements in causal modeling procedures have stemmed from the work of a number of individuals, including Joreskog and his colleagues (e.
Rather than just settle for "popular" arguments garnered from routine cross-tabular survey analysis, causal modeling identifies the messages that have the power to actually change attitudes and influence behavior.
As we shall see, in the case of the causal modeling techniques Humphreys interprets this idea about invariance as requiring that the functional forms representing causal relationships be additive.
Just as the ERM system continuously monitors risks and uses causal modeling to assess their potential impact on AEU, it also continuously evaluates AEU's potential risk mitigation actions.
They present 23 journal reprints (originally published between 1963 and 2004) in sections that deal with research design and study outcomes, quantitative issues in sampling, issues in measurement, descriptive analysis of quantitative data, and causal modeling.
Sophisticated statistical procedures, for instance, using inferred causal modeling (e.