Due to unmeasured confounding, it is often not possible to identify causal
effects from a postulated model. Nevertheless, we can ask for partial
identification, which usually boils down to finding upper and lower bounds of a
causal quantity of interest derived from all solutions compatible with the
encoded structural assumptions. One appealing way to derive such bounds is by
casting it in terms of a constrained optimization method that searches over all
causal models compatible with evidence, as introduced in the classic work of
Balke and Pearl (1994) for discrete data. Although by construction this
guarantees tight bounds, it poses a formidable computational challenge. To cope
with this issue, alternatives include algorithms that are not guaranteed to be
tight, or by introducing restrictions on the class of models. In this paper, we
introduce a novel alternative: inspired by ideas coming from belief
propagation, we enforce compatibility between marginals of a causal model and
data, without constructing a global causal model. We call this collection of
locally consistent marginals the causal marginal polytope. As global
independence constraints disappear when considering small dimensional tractable
marginals, this also leads to a rethinking of how to elicit and express causal
knowledge. We provide an explicit algorithm and implementation of this idea,
and assess its practicality with numerical experiments.

本文提出了一种基于置信传播的方法，通过在满足证据的所有因果模型中搜索来推导一个目标因果量的上下界，同时确保因果模型的边缘与数据相容，而不必构建一个全局因果模型，从而提出一种新的局部一致边缘的集合，称为因果边缘多面体。