Graphical models | Causal inference | Independence (probability theory)
In statistics and causal graphs, a variable is a collider when it is causally influenced by two or more variables. The name "collider" reflects the fact that in graphical models, the arrow heads from variables that lead into the collider appear to "collide" on the node that is the collider. They are sometimes also referred to as inverted forks. The causal variables influencing the collider are themselves not necessarily associated. If they are not adjacent, the collider is unshielded. Otherwise, the collider is shielded and part of a triangle. The result of having a collider in the path is that the collider blocks the association between the variables that influence it. Thus, the collider does not generate an unconditional association between the variables that determine it. Conditioning on the collider via regression analysis, stratification, experimental design, or sample selection based on values of the collider create a non-causal association between X and Y (Berkson's paradox). In the terminology of causal graphs, conditioning on the collider opens the path between X and Y. This will introduce bias when estimating the causal association between X and Y, potentially introducing associations where there are none. Colliders can therefore undermine attempts to test causal theories. Colliders are sometimes confused with confounder variables. Unlike colliders, confounder variables should be controlled for when estimating causal associations. (Wikipedia).
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From playlist Statistics
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https://www.patreon.com/ProfessorLeonard Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
From playlist Statistics (Full Length Videos)
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Introduction to Correlation
From playlist Statistics
This educational video delves into how you quantify a linear statistical relationship between two variables using covariance! #statistics #probability #SoME2 This video gives a visual and intuitive introduction to the covariance, one of the ways we measure a linear statistical relation
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Statistical Rethinking 2022 Lecture 05 - Elemental Confounds
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From playlist Statistical Rethinking 2022
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From playlist Statistical Rethinking 2022
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From playlist Statistical Rethinking 2023
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From playlist Statistical Rethinking 2023
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From playlist Statistical Rethinking Winter 2019
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