causal

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

causal

of or implying a cause; relating to or of the nature of cause and effect: a causal factor
Not to be confused with:
casual – happening by chance; unexpected; fortuitous: a casual meeting; not dressy: a casual event

caus·al

 (kô′zəl)
adj.
1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices.
2. Indicative of or expressing a cause.
n.
A word or grammatical element, such as since or because, expressing a cause or reason.

caus′al·ly adv.

causal

(ˈkɔːzəl)
adj
1. acting as or being a cause
2. stating, involving, or implying a cause: the causal part of the argument.
3. (Philosophy) philosophy (of a theory) explaining a phenomenon or analysing a concept in terms of some causal relation
ˈcausally adv

caus•al

(ˈkɔ zəl)

adj.
1. of or pertaining to a cause.
2. expressing a cause, as the conjunctions because and since.
[1520–30; < Latin]
caus′al•ly, adv.
ThesaurusAntonymsRelated WordsSynonymsLegend:
Adj.1.causal - involving or constituting a cause; causing; "a causal relationship between scarcity and higher prices"
causative - producing an effect; "poverty as a causative factor in crime"
Translations
kauzálnípříčinný

causal

[ˈkɔːzəl] ADJcausal

causal

[ˈkɔːzəl] adj [link, relationship, connection] → causal(e)

causal

adjkausal, ursächlich; causal relationshipKausalzusammenhang m

causal

[ˈkɔːzl] adjcausale

causal

a. causal.
References in periodicals archive ?
Tucked away in ACEs research, often toward the end of studies, is the all too common phrase that their studies are not able to discern "causal inference", meaning we don't really know if ACEs caused the bad outcome in the study, or, if it does, we don't know by how much, or even how.
However, to quantify the causal inference produced, statistical techniques are commonly used that contrast the association among the variables of interest, not precisely of causal effect.
What does "causal inference" mean, and how can we use it to make decisions?
Inference on the relationship between cause and effect is called causal inference. Some of the major methods of causal inference are illustrated below (The Economics of Cause and Effect - Thought Process that Catches Truth from Data, Makiko Nakamuro and Yusuke Tsugawa, Diamond, Feb.
(5) Judea Pearl, "Causal Inference in Statistics: An Overview," Statistics Surveys 3 (2009): 96-146, doi:10.1214/09-SS057; and Christopher Winship and Michael Sobel, "Causal Inference in Sociological Studies," in Handbook of Data Analysis, ed.
In this example, gender is called a confounder in causal inference literatures.[7] Although the new instruction method increases the score of both boys and girls, the imbalance of the gender distribution in two schools may confound the effect of the new instruction method.
Nutritional epidemiology is one of the most challenging fields in epidemiology with respect to causal inference. Eating or not eating a particular food often occurs within a much wider dietary habit pattern, thus making it difficult to statistically disentangle the effects of individual foods.
Causal inference was performed to evaluate the causal relationship between a causal variable and the target variable.
Whatever Phase II and III clinical trials or CER, properly designed and carried out RCTs can provide the most definitive causal inference. Compared with RCTs of Phase II and III, which can produce persuasive causal inference, the challenge for CER should be considered cautiously because the cleanest comparison occurs only when standard interventions are performed, i.e., there are no unexpected co-interventions, such as medications, supplementary therapies, and behaviors during trials.
Advanced causal inference methods are an appealing approach to meeting this need because such methods can reduce biases stemming from imbalances in observed characteristics (when assignment is nonrandom) in patients with and without medical homes.