Do operator: Difference between revisions

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The '''''do'' operator''' is used in causal inference to denote an intervention. Given random variables <math>X,Y</math>, we write <math>\Pr(Y=y \mid \mathit{do}(X=x))</math> to mean the [[probability]] that <math>Y=y</math> given we intervene and set <math>X</math> to be <math>x</math>.
The '''''do'' operator''' is used in causal inference to denote an intervention. Given random variables <math>X,Y</math>, we write <math>\Pr(Y=y \mid \mathit{do}(X=x))</math> to mean the [[probability]] that <math>Y=y</math> given we intervene and set <math>X</math> to be <math>x</math>. In some texts, this is abbreviated to <math>\Pr(y\mid\hat x)</math>.


In general <math>\Pr(Y=y \mid \mathit{do}(X=x))</math> is not the same as conditioning on <math>X=x</math>, i.e. <math>\Pr(Y=y \mid X=x)</math>.
In general <math>\Pr(Y=y \mid \mathit{do}(X=x))</math> is not the same as conditioning on <math>X=x</math>, i.e. <math>\Pr(Y=y \mid X=x)</math>.

Revision as of 20:29, 2 June 2018

The do operator is used in causal inference to denote an intervention. Given random variables X,Y, we write Pr(Y=ydo(X=x)) to mean the probability that Y=y given we intervene and set X to be x. In some texts, this is abbreviated to Pr(yx^).

In general Pr(Y=ydo(X=x)) is not the same as conditioning on X=x, i.e. Pr(Y=yX=x).