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Directional Separation (d-Separation)
Directional separation (d-separation) is a graphical criterion used to determine whether a set of variables is conditionally independent of another set in a directed acyclic graph (DAG). In a directed graph, two variables and are d-separated by a set of conditioning variables if there is no active path connecting them, meaning they are conditionally independent given . Understanding d-separation is essential for proper experimental design and observational analysis to prevent biases, such as post-treatment bias, which occurs when conditioning on an intermediate variable (like a mediator) blocks the true causal pathway or introduces spurious associations.
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Bayesian Statistics
Statistics
Data Science