Do-calculus when the True Graph Is Unknown

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.

Authors

• Antti Hyttinen
• Frederick Eberhardt
• Matti Järvisalo

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