Causal graph
Causal Graph
A Causal Graph (pronunciation: /ˈkɔːzəl ɡrɑːf/) is a directed graph that is used to visually represent and analyze the causes in a causal system.
Etymology
The term "Causal Graph" is derived from the Latin word "causa" meaning "cause" and the Greek word "graph" meaning "writing" or "drawing".
Definition
A Causal Graph is a Directed Acyclic Graph (DAG) where the nodes represent variables and the edges represent causal relationships between the variables. The direction of the edges indicates the direction of causality, from cause to effect.
Related Terms
- Causal Model: A mathematical model expressing a causal relationship.
- Causal Inference: The process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.
- Causal Loop Diagram: A causal loop diagram (CLD) is a causal diagram that aids in visualizing how different variables in a system are interrelated.
- Causal Network: A causal network is a Bayesian network with an explicit requirement that the relationships be causal.
Usage
Causal Graphs are used in various fields such as Epidemiology, Economics, Machine Learning, and Statistics to understand and predict the behavior of complex systems. They are particularly useful in identifying confounding variables and in designing experiments.
See Also
External links
- Medical encyclopedia article on Causal graph
- Wikipedia's article - Causal graph
This WikiMD article is a stub. You can help make it a full article.
Languages: - East Asian
中文,
日本,
한국어,
South Asian
हिन्दी,
Urdu,
বাংলা,
తెలుగు,
தமிழ்,
ಕನ್ನಡ,
Southeast Asian
Indonesian,
Vietnamese,
Thai,
မြန်မာဘာသာ,
European
español,
Deutsch,
français,
русский,
português do Brasil,
Italian,
polski