Towards Bounding Causal Effects under Markov Equivalence
Alexis Bellot
Predicting the effect of unseen interventions is a fundamental research
question across the data sciences. It is well established that, in general,
such questions cannot be answered definitively from observational data, e.g.,
as a consequence of unobserved confounding. A generalization of this task is to
determine non-trivial bounds on causal effects induced by the data, also known
as the task of partial causal identification. In the literature, several
algorithms have been developed for solving this problem. Most, however, require
a known parametric form or a fully specified causal diagram as input, which is
usually not available in practical applications. In this paper, we assume as
input a less informative structure known as a Partial Ancestral Graph, which
represents a Markov equivalence class of causal diagrams and is learnable from
observational data. In this more "data-driven" setting, we provide a systematic
algorithm to derive bounds on causal effects that can be computed analytically.