Reichenbach's principle asserts that if two observed variables are found to
be correlated, then there should be a causal explanation of these correlations.
Furthermore, if the explanation is in terms of a common cause, then the
conditional probability distribution over the variables given the complete
common cause should factorize. The principle is generalized by the formalism of
causal models, in which the causal relationships among variables constrain the
form of their joint probability distribution. In the quantum case, however, the
observed correlations in Bell experiments cannot be explained in the manner
Reichenbach's principle would seem to demand. Motivated by this, we introduce a
quantum counterpart to the principle. We demonstrate that under the assumption
that quantum dynamics is fundamentally unitary, if a quantum channel with input
A and outputs B and C is compatible with A being a complete common cause of B
and C, then it must factorize in a particular way. Finally, we show how to
generalize our quantum version of Reichenbach's principle to a formalism for
quantum causal models, and provide examples of how the formalism works.

通过创造量子情况下的类 - Reichenbach 原则，研究了量子中的因果关系及其对于概率分布的制约形式，并推广到形式化的量子因果模型。

量子共同原因与量子因果模型

Quantum common causes and quantum causal models

A directed acyclic graph (DAG) partially represents the conditional
independence structure among observations of a system if the local Markov
condition holds, that is, if every variable is independent of its
non-descendants given its parents. In general, there is a whole class of DAGs
that represents a given set of conditional independence relations. We are
interested in properties of this class that can be derived from observations of
a subsystem only. To this end, we prove an information theoretic inequality
that allows for the inference of common ancestors of observed parts in any DAG
representing some unknown larger system. More explicitly, we show that a large
amount of dependence in terms of mutual information among the observations
implies the existence of a common ancestor that distributes this information.
Within the causal interpretation of DAGs our result can be seen as a
quantitative extension of Reichenbach's Principle of Common Cause to more than
two variables. Our conclusions are valid also for non-probabilistic
observations such as binary strings, since we state the proof for an
axiomatized notion of mutual information that includes the stochastic as well
as the algorithmic version.

该研究讨论了如何通过观察子系统来推断一个未知的大系统中的公共祖先，使用了信息论不等式来量化观察结果中的依赖关系，将多变量情况下的 Reichenbach 原理推广到因果解释的有向无环图中，并考虑了非概率观察结果的情况。