Do operator: Difference between revisions

From Machinelearning
No edit summary
No edit summary
Line 1: Line 1:
The '''''do'' operator''' is used in causal inference to denote an intervention. Given [[random variable]]s <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> (this notation assumes that the random variables corresponding to the individual values are clear from context). The notation <math>\Pr_x(y)</math> is also used.
The '''''do'' operator''' is used in causal inference to denote an intervention. Given [[random variable]]s <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> (this notation assumes that the random variables corresponding to the individual values are clear from context). The notation <math>\Pr_x(y)</math> is also used.


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>. Note also that in the expression <math>\mathit{do}(X=x)</math>, the subexpression <math>X=x</math> does not mean the event where the random variable <math>X</math> takes on the value <math>x</math>, i.e. the event <math>\{\omega\in\Omega : X(\omega) = x\}</math>. Thus, inside a ''do'' operator, the standard notational convention of probability theory does not hold.


The ''do'' operator is used extensively in the [[Do calculus|''do'' calculus]].
The ''do'' operator is used extensively in the [[Do calculus|''do'' calculus]].

Revision as of 23:07, 14 February 2019

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^) (this notation assumes that the random variables corresponding to the individual values are clear from context). The notation Prx(y) is also used.

In general Pr(Y=ydo(X=x)) is not the same as conditioning on X=x, i.e. Pr(Y=yX=x). Note also that in the expression do(X=x), the subexpression X=x does not mean the event where the random variable X takes on the value x, i.e. the event {ωΩ:X(ω)=x}. Thus, inside a do operator, the standard notational convention of probability theory does not hold.

The do operator is used extensively in the do calculus.

History

Pearl: "An equivalent notation, using set(x) instead of do(x), was used in Pearl (1995a). The do(x) notation was first used in Goldszmidt and Pearl (1992) and is gaining in popular support. Lauritzen (2001) used P(yXx). The expression P(ydo(x)) is equivalent in intent to P(Yx=y) in the potential-outcome model introduced by Neyman (1923) and Rubin (1974) and to the expression P[(X=x)(Y=y)] in the counter-factual theory of Lewis (1973b)."[1]

References

  1. Judea Pearl. Causality. p. 70, footnote 2