About: Causal graph

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Causal graph Goto Sponge NotDistinct Permalink

An Entity of Type : dbo:Person, within Data Space : dbpedia.org associated with source document(s)
QRcode icon

http://dbpedia.org/describe/?url=http%3A%2F%2Fdbpedia.org%2Fresource%2FCausal_graph

In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs (also known as path diagrams, causal Bayesian networks or DAGs) are probabilistic graphical models used to encode assumptions about the data-generating process.

Attributes	Values
rdf:type	person
rdfs:label	Causal graph (en)
rdfs:comment	In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs (also known as path diagrams, causal Bayesian networks or DAGs) are probabilistic graphical models used to encode assumptions about the data-generating process. (en)
foaf:depiction
dcterms:subject	Graphical models
Wikipage page ID	44239711 (xsd:integer)
Wikipage revision ID	1109084450 (xsd:integer)
Link from a Wikipage to another Wikipage	Bayesian network Graphical models Path analysis (statistics) Graphical models Bayesian networks Endogenous Sewall Wright Directed Acyclic Graph Testable Structural equation model
sameAs	Causal graph Causal graph Causal graph Causal graph
dbp:wikiPageUsesTemplate	dbt:'s dbt:Clear dbt:Reflist
thumbnail	wiki-commons:Special:FilePath/College_notID.png?width=300
has abstract	In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs (also known as path diagrams, causal Bayesian networks or DAGs) are probabilistic graphical models used to encode assumptions about the data-generating process. Causal graphs can be used for communication and for inference. As communication devices, the graphs provide formal and transparent representation of the causal assumptions that researchers may wish to convey and defend. As inference tools, the graphs enable researchers to estimate effect sizes from non-experimental data, derive testable implications of the assumptions encoded, test for external validity, and manage missing data and selection bias. Causal graphs were first used by the geneticist Sewall Wright under the rubric "path diagrams". They were later adopted by social scientists and, to a lesser extent, by economists. These models were initially confined to linear equations with fixed parameters. Modern developments have extended graphical models to non-parametric analysis, and thus achieved a generality and flexibility that has transformed causal analysis in computer science, epidemiology, and social science. (en)
gold:hypernym	Models
prov:wasDerivedFrom	wikipedia-en:Causal_graph?oldid=1109084450&ns=0
page length (characters) of wiki page	12360 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Causal_graph
is Link from a Wikipage to another Wikipage of	Quantum logic Root cause analysis Bayesian network Material conditional Causal Markov condition Sewall Wright Causal Graphs Causal graphs
is Wikipage redirect of	Causal Graphs Causal graphs
is known for of	Sewall Wright
is known for of	Sewall Wright
is foaf:primaryTopic of	wikipedia-en:Causal_graph

Faceted Search & Find service v1.17_git139 as of Feb 29 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3330 as of Mar 19 2024, on Linux (x86_64-generic-linux-glibc212), Single-Server Edition (378 GB total memory, 62 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software