causal

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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 ?
Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems.
These include a causal modeling approach for estimating the potential future outcomes that would result from multiple decision alternatives, which improves lenders' ability to make lending-related decisions.
In fact, even the tasks of description and causal modeling must be informed by such ideas and ideals, if they are adequately to serve their intended purposes," said Sanjay G.
He then utilizes Pearl's 'surgical' account of causal modeling to further develop the position.
Mulaik (emeritus psychology, Georgia Institute of Technology) writes specifically for quantitative methodologists and graduate students in methodological programs, but suggests that the book would also be useful for researchers and graduate students in the behavioral and social sciences who are seeking a deeper understanding of causation, linear causal modeling, and structural equation modeling than provided in standard texts.
He presents models for multiple regressions, multinomials, ordered logistic regression, La Guinier models, multi-level models, late and variable models, factor analysis, analysis of simultaneous equation models, causal modeling in simultaneous equation models, digital data analysis, and models for large groups of data in event histories.
John Morton, an established leader and author in the field of cognition and development, addresses this debate by challenging readers to incorporate cognition as an additional etiological source in his causal modeling approach to understanding developmental disorders.