causal

(redirected from causal inference)
Also found in: Dictionary, Thesaurus, Legal, Encyclopedia.

causal

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

causal

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 ?
The Blakely decision raises a dilemma for causal inference for three reasons.
The explicit recognition of this complexity in the econometric literature has promoted the development of sophisticated empirical (and theoretical) modeling to allow causal inferences regarding human behavior.
In some cases when true experiments are not performed, and even when structural modeling is not used, we still may be able to reach causal inferences with some degree of confidence.
Therefore, the issue is how to deal with this analytical reality and improve the quality of causal inference.
By prioritizing causal inferences, van den Broek's model leaves out much visual information that could not fit into propositional sets of necessity and sufficiency.
The fundamental problem of causal inference, in criminological research as elsewhere, is that it is impossible to observe the value of [Y.
The fundamental challenge in using these data for causal inference is addressing potential confounding while still retaining the representativeness of the data.
In particular, it is still not entirely clear which benefits natural experiments bring in terms of causal inference.
The causal inference can be approximated by a visual analysis of the patterns of sequentiality between peaks in media attention (lines) and peaks in government action (dots).
Some studies are cross-sectional in nature, making causal inference difficult, whereas others are small and evaluated too few outcomes to draw firm conclusions.
However, several types of confounding can influence the results of a Mendelian randomization study, causal inference in observational research requires caution, and the interpretation of null Mendelian randomization studies is challenging, especially in the common situation of a weak association between the gene and the biomarker.
Individual chapters discuss basic tools; simple and stratified sampling; cluster sampling; graphics; ratios and linear regression; categorical data regression; post-stratification, raking, and calibration; two-phase sampling; missing data; and causal inference.