@inproceedings{HyttinenEJ:UAI2015,
author = {Antti Hyttinen and Frederick Eberhardt and Matti J\"arvisalo},
title = {Do-calculus when the True Graph is Unknown},
editor = {Tom Heskes and Marina Meila},
booktitle = {Proceedings of the 31st Conference on
Uncertainty in Artificial Intelligence (UAI 2015)},
pages = {395--404},
year = {2015},
publisher = {AUAI Press},
}
Abstract:
One of the basic tasks of causal discovery is to estimate the
causal effect of some set of variables on another given a
statistical data set. In this article we bridge the gap between
causal structure discovery and the $do$-calculus by proposing a
method for the identification of causal effects on the basis of
arbitrary (equivalence) classes of semi-Markovian causal models.
The approach uses a general logical representation of the
equivalence class of graphs obtained from a causal structure
discovery algorithm, the properties of which can then be queried
by procedures implementing the $do$-calculus inference for
causal effects. We show that the method is more efficient than
determining causal effects using a naive enumeration of graphs
in the equivalence class. Moreover, the method is complete with
respect to the identifiability of causal effects for settings,
in which extant methods that do not require knowledge of the
true graph, offer only incomplete results. The method is entirely
modular and easily adapted for different background settings.