Do operator

From Machinelearning
Revision as of 23:07, 14 February 2019 by IssaRice (talk | contribs)

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