Mahadevan Sridhar
Adobe Research, 345 Park Avenue, San Jose, CA 95110, USA.
Entropy (Basel). 2025 May 16;27(5):531. doi: 10.3390/e27050531.
Causal discovery involves searching intractably large spaces. Decomposing the search space into classes of observationally equivalent causal models is a well-studied avenue to making discovery tractable. This paper studies the topological structure underlying causal equivalence to develop a categorical formulation of Chickering's transformational characterization of Bayesian networks. A homotopic generalization of the Meek-Chickering theorem on the connectivity structure within causal equivalence classes and a topological representation of Greedy Equivalence Search (GES) that moves from one equivalence class of models to the next are described. Specifically, this work defines causal models as propable symmetric monoidal categories (cPROPs), which define a functor category CP from a coalgebraic PROP to a symmetric monoidal category C. Such functor categories were first studied by Fox, who showed that they define the right adjoint of the inclusion of Cartesian categories in the larger category of all symmetric monoidal categories. cPROPs are an algebraic theory in the sense of Lawvere. cPROPs are related to previous categorical causal models, such as Markov categories and affine CDU categories, which can be viewed as defined by cPROP maps specifying the semantics of comonoidal structures corresponding to the "copy-delete" mechanisms. This work characterizes Pearl's structural causal models (SCMs) in terms of Cartesian cPROPs, where the morphisms that define the endogenous variables are purely deterministic. A higher algebraic K-theory of causality is developed by studying the classifying spaces of observationally equivalent causal cPROP models by constructing their simplicial realization through the nerve functor. It is shown that Meek-Chickering causal DAG equivalence generalizes to induce a homotopic equivalence across observationally equivalent cPROP functors. A homotopic generalization of the Meek-Chickering theorem is presented, where covered edge reversals connecting equivalent DAGs induce natural transformations between homotopically equivalent cPROP functors and correspond to an equivalence structure on the corresponding string diagrams. The Grothendieck group completion of cPROP causal models is defined using the Grayson-Quillen construction and relate the classifying space of cPROP causal equivalence classes to classifying spaces of an induced groupoid. A real-world domain modeling genetic mutations in cancer is used to illustrate the framework in this paper.
因果发现涉及在难以处理的大空间中进行搜索。将搜索空间分解为观测等价因果模型的类别是一条经过充分研究的使发现变得易于处理的途径。本文研究因果等价背后的拓扑结构,以开发贝叶斯网络的奇克林变换特征的范畴表述。描述了关于因果等价类内连通性结构的米克 - 奇克林定理的同伦推广以及从一个模型等价类移动到下一个等价类的贪婪等价搜索(GES)的拓扑表示。具体而言,这项工作将因果模型定义为可概率对称幺半范畴(cPROPs),它从一个余代数PROP定义一个函子范畴CP到一个对称幺半范畴C。这样的函子范畴最早由福克斯研究,他表明它们定义了笛卡尔范畴包含在所有对称幺半范畴更大范畴中的右伴随。cPROPs在劳威尔意义上是一种代数理论。cPROPs与先前的范畴因果模型相关,如马尔可夫范畴和仿射CDU范畴,它们可以被视为由指定对应于“复制 - 删除”机制的余幺半结构语义的cPROP映射定义。这项工作根据笛卡尔cPROPs刻画了珀尔的结构因果模型(SCMs),其中定义内生变量的态射是纯确定性的。通过研究观测等价因果cPROP模型的分类空间,通过神经函子构造它们的单纯实现,发展了一种更高阶的因果代数K理论。结果表明,米克 - 奇克林因果有向无环图等价推广后可在观测等价的cPROP函子之间诱导同伦等价。给出了米克 - 奇克林定理的同伦推广版本,其中连接等价有向无环图的覆盖边反转在同伦等价的cPROP函子之间诱导自然变换,并对应于相应弦图上的等价结构。使用cPROP因果模型的格罗滕迪克群完备化通过格雷森 - 奎伦构造来定义,并将cPROP因果等价类的分类空间与诱导广群的分类空间相关联。本文使用一个模拟癌症基因突变的现实世界领域来阐述该框架。