Causal discovery aims to recover a causal graph from data generated by it; constraint based methods do so by searching for a d-separating conditioning set of nodes in the graph via an oracle. In this paper, we provide analytic evidence that on large graphs, d-separation is a rare phenomenon, even when guaranteed to exist, unless the graph is extremely sparse. We then provide an analytic average case analysis of the PC Algorithm for causal discovery, as well as a variant of the SGS Algorithm we call UniformSGS. We consider a set $V=\{v_1,\ldots,v_n\}$ of nodes, and generate a random DAG $G=(V,E)$ where $(v_a, v_b) \in E$ with i.i.d. probability $p_1$ if $a<b$ and $0$ if $a > b$. We provide upper bounds on the probability that a subset of $V-\{x,y\}$ d-separates $x$ and $y$, conditional on $x$ and $y$ being d-separable; our upper bounds decay exponentially fast to $0$ as $|V| \rightarrow \infty$. For the PC Algorithm, while it is known that its worst-case guarantees fail on non-sparse graphs, we show that the same is true for the average case, and that the sparsity requirement is quite demanding: for good performance, the density must go to $0$ as $|V| \rightarrow \infty$ even in the average case. For UniformSGS, while it is known that the running time is exponential for existing edges, we show that in the average case, that is the expected running time for most non-existing edges as well.
翻译:构造发现的目的是从由它生成的数据中恢复一个因果图表; 限制法的方法是通过一个甲骨文, 在图形中寻找一个 d 分离的节点设置。 在本文中, 我们提供分析证据表明, 在大图表中, d 分离是一个罕见的现象, 即便该图表保证存在, 除非该图极少。 我们然后对 PC 的因果关系进行分析性平均案例分析, 以及 SGS 的 SGS 内部端点变量, 我们称之为 统一SGS。 我们考虑用一个 $V_ v_ 1,\\ redolfthm 来设置一个 d- 调点。 我们考虑用一个 $V_ v_ 1, v_n_ 节节点来设置一个 d- 调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调的调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调。 $ $ $ $ $ $ 美元 美元 美元, 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元 美元</s>