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The completed partially directed acyclic graph of a Markov equivalence of directed acyclic graphs class has their common skeleton of a directed graph. An edge is directed exactly when its orientation agrees in every member of the class, and is otherwise undirected. It can be constructed by enumerating all acyclic orientations with the prescribed unshielded colliders and retaining only the common directions; practical PC algorithms use orientation propagation instead of enumeration.

The global Markov property for a directed acyclic graph requires, for all pairwise disjoint vertex sets $A,B,C$, that D-separation of $A$ and $B$ by $C$ in $G$ implies $Z_A\perp Z_B\mid Z_C$. Two Markov equivalent directed acyclic graphs encode exactly the same such D-separations. The structural characterization is that they have the same skeleton of a directed graph and the same unshielded colliders. A distribution has faithfulness of a directed acyclic graph when its conditional independences are exactly those encoded by D-separation; this includes the global Markov implication as well as its converse.

The population PC algorithm uses an exact conditional independence oracle. Start with the complete undirected graph, and conditioning-set size $\ell =0$. At each size, examine ordered adjacent pairs $(u,v)$. Test subsets $C\subseteq \mathrm{Adj}(u)\setminus \{v\}$ of size $\ell$ until independence $Z_u\perp Z_v\mid Z_C$ is found or all such subsets have been examined. On finding it, delete $u-v$ and record a symmetric separating set $\mathrm{Sep}(u,v)=\mathrm{Sep}(v,u)=C$. Ordered pairs ensure that subsets from either endpoint's neighbours are considered. Increase $\ell$ until no adjacent ordered pair has enough remaining neighbours to supply a set of that size. Rechecking eligibility after deletion, or freezing the neighbours at each level and then deleting, gives the same population skeleton under the hypothesis below.

Next, for each unshielded triple $u-v-w$ with $u,w$ nonadjacent, orient $u\to v\leftarrow w$ precisely when $v\notin \mathrm{Sep}(u,w)$. Propagate directions that are forced without introducing a directed cycle or an additional unshielded collider. In particular, $a\to b-c$ with $a,c$ nonadjacent forces $b\to c$; an undirected edge $a-b$ with a directed path from $a$ to $b$ forces $a\to b$; and $a-b,a-c,a-d,c\to b,d\to b$ with $c,d$ nonadjacent forces $a\to b$. The first avoids a new unshielded collider, the second a directed cycle. For the third, $b\to a$ would force $c\to a$ and $d\to a$ to avoid cycles, creating the prohibited unshielded collider $c\to a\leftarrow d$.

For a fully specified exact completion, list all acyclic orientations of the recovered skeleton which extend the collider arrows and have exactly the recorded unshielded colliders. Direct an edge if and only if all listed graphs orient it the same way; otherwise leave it undirected. This finite enumeration implements the definition of the completed partially directed acyclic graph; orientation propagation is the usual efficient way to obtain the same completion. The equivalence-class proof below uses the exact completion description and does not need an unproved completeness claim about a chosen subset of propagation rules.

To prove correctness, assume faithfulness of a directed acyclic graph to $G^0$. A true edge $u\to v$ cannot be D-separated by any set excluding its endpoints: its length-one path is always active. Thus a true edge is never deleted. Conversely, take nonadjacent $u,v$ and choose their order so that $u$ is earlier than $v$ in a topological ordering of $G^0$. Then $u$ is a nondescendant and nonparent of $v$. Its parent set $\mathrm{Pa}(v)$ gives D-separation of $u$ and $v$. To see this local graphical fact, a path beginning $v\leftarrow q$ is blocked by the conditioned parent $q$, a noncollider. A path beginning with an arrow out of $v$ must encounter a first collider before reaching the nondescendant $u$. That collider is a descendant of $v$, and neither it nor any of its descendants can be a parent of $v$ in an acyclic graph. Hence conditioning on $\mathrm{Pa}(v)$ does not open it.

All parent edges survive. If $u-v$ has not already been deleted, $\mathrm{Pa}(v)$ remains among $v$'s neighbours excluding $u$. It is therefore tested by size $|\mathrm{Pa}(v)|$, and faithfulness forces deletion. The size loop cannot stop prematurely while this set remains eligible. This proves exact recovery of the skeleton of a directed graph.

For an unshielded triple $u-v-w$, if it is a noncollider in $G^0$, its length-two path remains active unless $v$ is conditioned on. Thus every separator of $u,w$ contains $v$. If it is a collider, conditioning on $v$ opens the length-two path, so no separator can contain $v$. Faithfulness turns these graphical statements into the recorded separator test. Therefore the collider phase recovers precisely all unshielded colliders. By the skeleton and collider characterization of Markov equivalence, the acyclic extensions with exactly these colliders are exactly the Markov equivalence of directed acyclic graphs class of $G^0$. In particular that set is nonempty, and the completion returns its CPDAG. This proves population PC correctness under faithfulness.

For the four-variable example, the only removable pairs are $2,4$, with separator $\varnothing$, and $1,3$, with separator $\{2,4\}$. The remaining skeleton has edges $1-2,1-4,2-3,3-4$. The unshielded triples $2-1-4$ and $2-3-4$ both have center outside the separator of $2,4$, so both are unshielded colliders. Every remaining edge is thereby oriented:The other two unshielded triples are noncolliders, consistent with the separator $\{2,4\}$ of $1,3$. There is no undirected edge left, so the equivalence class consists of this unique directed acyclic graph. This identification is about the given faithful observational distribution; general PC algorithm outputs need not identify a unique causal direction.

The PC algorithm learns conditional-independence structure from a collection of observed random variables. Its standard initialization is the complete undirected graph, which represents every pair as a candidate adjacency. It then tests conditional independence over increasing conditioning-set sizes, using subsets of current neighboring vertices. Whenever $X\perp Y\mid S$ is established, it removes $X-Y$ and records $S$ as a separating set.^[ref]

For an unshielded triple $X-Z-Y$ with $X,Y$ nonadjacent, a recorded separating set that excludes $Z$ supports the collider orientation $X\to Z\leftarrow Y$. Further orientation rules propagate directions while avoiding directed cycles and unsupported new collider patterns. For example, a chain $X\to Z\to Y$ typically has $X\perp Y\mid Z$, so its separating set contains $Z$; a collider $X\to Z\leftarrow Y$ instead has marginal independence in the simple three-variable graph, so the empty separating set identifies the collider.

The oracle guarantee assumes an acyclic causal model over the observed variables, causal sufficiency, correct conditional independence decisions and faithfulness of a directed acyclic graph. The output represents a class under Markov equivalence of directed acyclic graphs; it need not identify every arrow of one unique causal graph.

An arbitrary incomplete initial graph does not satisfy that guarantee. With only two genuinely adjacent dependent variables, starting with their edge absent cannot recover it: the skeleton stage only removes edges. PC initialization from a conditional independence graph is a qualified screening construction that retains the true skeleton and records the required separating information for absent pairs. In finite samples, incorrect early independence decisions can propagate through both edge removal and orientation.

Richard Scheines studies causal discovery: learning causal structure from statistical evidence under explicit assumptions. With Peter Spirtes, Clark Glymour and collaborators, he developed the Tetrad research programme, connecting causal inference, algorithms and machine learning.^[ref]

In Causation, Prediction, and Search by Peter Spirtes, Clark Glymour and Richard Scheines, the collaborators examine how patterns of conditional independence constrain a causal directed acyclic graph. The connection is informative but conditional: faithfulness of a directed acyclic graph, restrictions on unobserved causes and the correctness of the independence information affect what can be identified. Different graphs can remain indistinguishable under Markov equivalence of directed acyclic graphs, so observational data need not select one unique causal story.^[ref]