Do operator

From Machinelearning
Revision as of 23:04, 14 February 2019 by IssaRice (talk | contribs)

The do operator is used in causal inference to denote an intervention. Given random variables , we write to mean the probability that given we intervene and set to be . In some texts, this is abbreviated to (this notation assumes that the random variables corresponding to the individual values are clear from context). The notation is also used.

In general is not the same as conditioning on , i.e. .

The do operator is used extensively in the do calculus.

History

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

References

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