# Conditional independence

## Probability theory concept / From Wikipedia, the free encyclopedia

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In probability theory, **conditional independence** describes situations wherein an observation is irrelevant or redundant when evaluating the certainty of a hypothesis. Conditional independence is usually formulated in terms of conditional probability, as a special case where the probability of the hypothesis given the uninformative observation is equal to the probability without. If $A$ is the hypothesis, and $B$ and $C$ are observations, conditional independence can be stated as an equality:

- $P(A\mid B,C)=P(A\mid C)$

where $P(A\mid B,C)$ is the probability of $A$ given both $B$ and $C$. Since the probability of $A$ given $C$ is the same as the probability of $A$ given both $B$ and $C$, this equality expresses that $B$ contributes nothing to the certainty of $A$. In this case, $A$ and $B$ are said to be **conditionally independent** given $C$, written symbolically as: $(A\perp \!\!\!\perp B\mid C)$. In the language of causal equality notation, two functions $f(y)$ and $g(y)$ which both depend on a common variable $y$ are described as conditionally independent using the notation $f\left(y\right)~{\overset {\curvearrowleft \curvearrowright }{=}}~g\left(y\right)$, which is equivalent to the notation $P(f\mid g,y)=P(f\mid y)$.

The concept of conditional independence is essential to graph-based theories of statistical inference, as it establishes a mathematical relation between a collection of conditional statements and a graphoid.