Weakening faithfulness: some heuristic causal discovery algorithms
 769 Downloads
 1 Citations
Abstract
We examine the performance of some standard causal discovery algorithms, both constraintbased and scorebased, from the perspective of how robust they are against (almost) failures of the Causal Faithfulness Assumption. For this purpose, we make only the socalled TriangleFaithfulness assumption, which is a fairly weak consequence of the Faithfulness assumption, and otherwise allows unfaithful distributions. In particular, we allow violations of AdjacencyFaithfulness and OrientationFaithfulness. We show that the (conservative) PC algorithm, a representative constraintbased method, can be made more robust against unfaithfulness by incorporating elements of the GES algorithm, a representative scorebased method; similarly, the GES algorithm can be made less errorprone by incorporating elements of the conservative PC algorithm. As our simulations demonstrate, the increased robustness seems to matter even when faithfulness is not exactly violated, for with only finite sample, distributions that are not exactly unfaithful may be sufficiently close to being unfaithful to make trouble.
Keywords
Causal discovery Faithfulness PC GES1 Introduction
Although it is usually more reliable to infer causal relations from experimental data than from observational data, the problem of causal discovery from observational data has been drawing increasingly more attention, thanks on the one hand to the practical difficulties in carrying out welldesigned experiments and on the other hand to the relative ease of obtaining large volumes of data from various records and measurements. A widely adopted framework is to use graphs to represent causal structures and to relate causal graphs to probability distributions via various assumptions. Two wellknown assumptions are known as the Causal Markov Assumption and the Causal Faithfulness Assumption [15]. The Causal Markov Assumption states that the joint distribution of a set of variables satisfies the Markov property of the true causal graph over them, or in other words, satisfies the conditional independence relations that are implied by the causal graph according to its Markov property. The Causal Faithfulness Assumption states the converse that the conditional independence relations satisfied by the joint distribution are all implied by the causal graph.
While the Causal Markov Assumption—when applied to causally sufficient systems where no common direct cause of two variables in the system is left out—is backed by substantial metaphysical principles relating causality and probability, such as Reichenbach’s principle of common cause [11], the Causal Faithfulness Assumption is usually taken to be a sort of Occam’s razor or methodological preference of simplicity [19]. As a result, the Causal Faithfulness Assumption is more dubious than the Causal Markov Assumption. Moreover, even if the Causal Faithfulness Assumption is not exactly violated, the distribution may be sufficiently close to being unfaithful to the causal graph in that a (conditional) dependence may be sufficiently weak to be almost indistinguishable from independence in finite samples (with a moderate sample size). The point that such “almost violations” of faithfulness pose serious challenges to causal discovery was already made in the relatively early days of the graphical modeling approach to causal discovery [7]. Recently, it was also established that the standard defense for the Causal Faithfulness Assumption that violations thereof are unlikely does not justify assuming away “almost unfaithfulness,” especially when the number of variables is large [17].
For these reasons, it is worth investigating, for those causal discovery methods that adopt the Causal Faithfulness Assumption, the extent to which the methods rely on the assumption, as well as the possibility of relaxing the assumption and adjusting the methods accordingly. The existing investigations of this sort have focused on the constraintbased approach to causal discovery [10, 14, 20]. In this paper, we follow this line of inquiry and bring the scorebased approach to bear on the problem. In particular, we argue that the (conservative) PC algorithm, a representative constraintbased method, can be made more robust against unfaithfulness by incorporating elements of the GES algorithm, a representative scorebased method, and that the GES algorithm can be made less errorprone by incorporating elements of the conservative PC algorithm.
The rest of the paper is organized as follows. We review the basic framework and notations in Sect. 2 and survey a number of consequences of the faithfulness assumption that may serve as weaker substitutes for the faithfulness assumption in Sect. 3. Then, in Sect. 4, we examine the behavior of PC and that of GES against certain kinds of unfaithfulness and motivate some natural hybrid algorithms. We test these hybrid algorithms through simulations in Sect. 5, which suggest, among other things, that the proposed algorithms are less errorprone than PC and GES at realistic sample sizes, even though the Faithfulness Assumption is not exactly violated. We conclude with discussions of some open problems in Sect. 6.
2 Preliminaries
We will use the following graph terminology. A (mixed) graph is a pair \(({\mathbf {V}},{\mathbf {E}})\), where \({\mathbf {V}}\) is a set of vertices (each representing a distinct random variable),^{1} and \({\mathbf {E}}\) is a set of edges between vertices such that between each pair of vertices there is at most one edge. For the purpose of this paper we need only two kinds of edges: directed (\(\rightarrow \)) and undirected (\(\!\!\!\)). Given a graph \({\mathcal {G}}({\mathbf {V}}, {\mathbf {E}})\) and any \(X, Y\in {\mathbf {V}}\), if there is an edge between X and Y of any kind, X and Y are said to be adjacent. If the edge is directed, e.g., \(X\rightarrow Y\), X is called a parent of Y and Y a child of X; if the edge is undirected, i.e., \(X\!\!\!Y\), then X and Y are called neighbors of each other. A path in \({\mathcal {G}}\) is a sequence of distinct vertices \((V_1, \ldots , V_n)\) such that for \(1\le i\le n1\), \(V_i\) and \(V_{i+1}\) are adjacent in \({\mathcal {G}}\). A path between \(V_1\) and \(V_n\) is called a directed path from \(V_1\) to \(V_n\) if the edge between \(V_i\) and \(V_{i+1}\) is \(V_i\rightarrow V_{i+1}\) for \(1\le i\le n1\). A vertex X is called an ancestor of a vertex Y and Y a descendant of X in \({\mathcal {G}}\) if \(X=Y\) or there is a directed path from X to Y in \({\mathcal {G}}\).
An (ordered) triple of vertices (X, Y, Z) in \({\mathcal {G}}\) is called an unshielded triple if X, Y are adjacent and Y, Z are adjacent, but X, Z are not adjacent. It is called a shielded triple or triangle if in addition to X, Y and Y, Z, X and Z are also adjacent. An unshielded or shielded triple (X, Y, Z) is called a collider if the edge between X and Y and the edge between Z and Y are both directed at Y, i.e., \(X\rightarrow Y\leftarrow Z\). Otherwise, it is called a noncollider.
A graph with only directed edges is called a directed graph, and a directed acyclic graph (DAG) is a directed graph in which no two distinct vertices are ancestors of each other. We assume that we are working with a set of variables \({\mathbf {V}}\), the underlying causal structure of which can be represented by a DAG over \({\mathbf {V}}\). A DAG entails a set of conditional independence statements according to its (local or global) Markov property. One statement of the (global) Markov property uses the notion of dseparation [8]. Given a path \((V_1,\ldots ,V_n)\) in a DAG, \(V_i\) (\(1<i<n\)) is said to be a collider (noncollider) on the path if the triple \((V_{i1}, V_i, V_{i+1})\) is a collider (noncollider). Given a set of vertices \({\mathbf {Z}}\), a path is blocked by \({\mathbf {Z}}\) if some noncollider on the path is in \({\mathbf {Z}}\) or some collider on the path has no descendant in \({\mathbf {Z}}\). For any \(X, Y\notin {\mathbf {Z}}\), X and Y are said to be dseparated by \({\mathbf {Z}}\) if every path between X and Y is blocked by \({\mathbf {Z}}\). For any \({\mathbf {X}}, {\mathbf {Y}}, {\mathbf {Z}}\) that are pairwise disjoint, \({\mathbf {X}}\) and \({\mathbf {Y}}\) are dseparated by \({\mathbf {Z}}\) if every vertex in \({\mathbf {X}}\) and every vertex in \({\mathbf {Y}}\) are dseparated by \({\mathbf {Z}}\). According to the (global) Markov property, a DAG entails that \({\mathbf {X}}\) and \({\mathbf {Y}}\) are independent conditional on \({\mathbf {Z}}\) if and only if \({\mathbf {X}}\) and \({\mathbf {Y}}\) are dseparated by \({\mathbf {Z}}\).
Two DAGs are said to be Markov equivalent if they entail the exact same conditional independence statements. A wellknown characterization is that two DAGs are Markov equivalent if and only if they have the same adjacencies (or skeleton, the undirected graph resulting from ignoring the direction of edges in a DAG) and the same unshielded colliders [18]. A Markov equivalence class of DAGs, \({\mathcal {M}}\), can be represented by a graph called a pattern (a.k.a an essential graph or complete partially directed acyclic graph); the pattern has the same adjacencies as every DAG in \({\mathcal {M}}\) such that for every X and Y that are adjacent, the pattern contains \(X\rightarrow Y\) if \(X\rightarrow Y\) appears in every DAG in \({\mathcal {M}}\), and contains \(X\!\!\!Y\) if \(X\rightarrow Y\) appears in some DAGs in \({\mathcal {M}}\) and \(X\leftarrow Y\) appears in others.
In this paper we assume that all variables in the given set \({\mathbf {V}}\) are observed, or in other words, the set of observed variables is causally sufficient so that we need not introduce latent variables to properly model the system. Under this simplifying assumption, the problem we are concerned with is that of inferring information about the causal DAG over \({\mathbf {V}}\) from i.i.d. data sampled from a joint distribution P over \({\mathbf {V}}\). All methods we know of make the following assumption:
Causal Markov Assumption: The joint distribution P over \({\mathbf {V}}\) is Markov to the true causal DAG \({\mathcal {G}}\) over \({\mathbf {V}}\) in the sense that every conditional independence statement entailed by \({\mathcal {G}}\) is satisfied by P.
And many methods also make the following assumption:^{2}
Causal Faithfulness Assumption: The joint distribution P over \({\mathbf {V}}\) is faithful to the true causal DAG \({\mathcal {G}}\) over \({\mathbf {V}}\) in the sense that every conditional independence statement satisfied by P is entailed by \({\mathcal {G}}\).
Under these two assumptions, the pattern of the true causal DAG is in principle determinable by the distribution, and many causal discovery algorithms aim to recover the pattern from data. In what follows, we will often omit the qualifier “causal” and refer to the assumptions simply as “Markov” and “Faithfulness,” respectively.
3 Weaker notions of faithfulness
As stated in Sect. 1, our work here follows a line of inquiry that seeks to adjust some standard causal discovery procedures to make them more robust against violations of Faithfulness. From this line of work a number of weaker notions of Faithfulness have emerged. Ramsey et al. [10] highlighted two consequences of the Faithfulness assumption (recall that we use \({\mathcal {G}}\) to denote the true causal DAG over \({\mathbf {V}}\), and P the true joint distribution of \({\mathbf {V}}\) from which samples are drawn):
AdjacencyFaithfulness: For every \(X, Y\in {\mathbf {V}}\), if X and Y are adjacent in \({\mathcal {G}}\), then they are not conditionally independent given any subset of \({\mathbf {V}}\backslash \{X, Y\}\).
 (i)
If (X, Y, Z) is a collider (i.e., \(X\rightarrow Y\leftarrow Z\)) in \({\mathcal {G}}\), then X and Z are not conditionally independent given any subset of \({\mathbf {V}}\backslash \{X, Z\}\) that includes Y.
 (ii)
Otherwise, X and Z are not conditionally independent given any subset of \({\mathbf {V}}\backslash \{X, Z\}\) that excludes Y.
A still weaker consequence of the Faithfulness assumption than AdjacencyFaithfulness is the following, first introduced by [20]:
 (i)
If (X, Y, Z) is a (shielded) collider in \({\mathcal {G}}\), then X and Z are not conditionally independent given any subset of \({\mathbf {V}}\backslash \{X, Z\}\) that includes Y.
 (ii)
Otherwise, X and Z are not conditionally independent given any subset of \({\mathbf {V}}\backslash \{X, Z\}\) that excludes Y.
SGSminimality: No proper subgraph of \({\mathcal {G}}\) satisfies the Markov assumption with P.
Where a proper subgraph of \({\mathcal {G}}\) is a DAG over \({\mathbf {V}}\) with a proper subset of the edges in \({\mathcal {G}}\). This is known as the Causal Minimality Condition in the literature [15]. To distinguish it from another minimality condition that is relevant to our discussion, we follow [19] to refer to it as SGSminimality (Spirtes, Glymour and Scheines’s minimality condition). Zhang and Spirtes [21] showed that it is in general very safe to assume the SGSminimality condition.
An interesting result is that if one assumes Markov, SGSminimality, and TriangleFaithfulness, then the rest of the Faithfulness assumption, including in particular AdjacencyFaithfulness as well as OrientationFaithfulness, can in principle be tested [14, 20].
In fact, the assumption needed to make the Faithfulness assumption testable is even weaker than TriangleFaithfulness (plus SGSminimality). It will be referred to as Pminimality (Pearl’s minimality condition, [9]):
Pminimality: No proper independencesubmodel of \({\mathcal {G}}\) satisfies the Markov assumption with P.
Where a proper independencesubmodel of \({\mathcal {G}}\) is a DAG over \({\mathbf {V}}\) that entails a proper superset of the conditional independence statements entailed by \({\mathcal {G}}\). In other words, the Pminimality assumption states that, for every DAG that entails all the conditional independence statements entailed by \({\mathcal {G}}\) plus some additional ones, P does not satisfy some of the additional ones entailed by the DAG.
Zhang [19] showed that Pminimality is weaker than TriangleFaithfulness plus SGSminimality, and if one assumes Markov and Pminimality, the Faithfulness assumption can in principle be tested. However, the Markov and Pminimality assumptions together are so weak that in general the adjacencies of the causal DAG over \({\mathbf {V}}\) are not uniquely determined by the distribution of \({\mathbf {V}}\). That is, there are cases where two DAGs over \({\mathbf {V}}\) have different adjacencies but both satisfy the Markov and Pminimality assumptions with a given distribution of \({\mathbf {V}}\). Here is an example that will prove relevant to our later discussion of the GES algorithm.
Example 1
Suppose \({\mathbf {V}} = \{V_1, V_2, V_3, V_4\}\), and the true causal structure is represented by the DAG in Fig. 1a. Suppose that the parameterization is such that the causal influence along the path \(V_1\rightarrow V_2\rightarrow V_4\) and that along the path \(V_1\rightarrow V_3\rightarrow V_4\) balance out. As a result, in addition to the conditional independence relations entailed by the graph, the distribution satisfies one (and only one) extra independence: Open image in new window , which is a violation of OrientationFaithfulness. Under such a circumstance, the DAG in Fig. 1b also satisfies the Markov and Pminimality assumptions with the distribution. (It is Markov because it entails only that Open image in new window , which is satisfied by the given distribution. It is Pminimal because every DAG that entails Open image in new window and some more conditional independence statements entails one that is not satisfied by the given distribution).
So the Markov and Pminimality assumptions together are not strong enough to entail that adjacencies are uniquely determined by a distribution. We do not know whether the Markov, SGSminimality and TriangleFaithfulness assumptions together are sufficiently strong to entail that adjacencies are uniquely determined by a distribution. In Example 1, obviously, the distribution is not TriangleFaithful to the DAG in Fig. 1b, for Open image in new window according to the distribution, which violates TriangleFaithfulness with respect to the triangle \((V_2, V_4, V_3)\). Indeed, in this example, given the Markov and TriangleFaithfulness assumptions the distribution entails that the adjacencies in the true causal DAG must be the same as those in Fig. 1a.
We conjecture that at least for linear models, adjacencies are in general uniquely determined by the true distribution under the Markov, SGSminimality and TriangleFaithfulness assumptions. If this is true, then TriangleFaithfulness (plus SGSminimality) is at once much weaker than (Adjacency)Faithfulness [20], and sufficiently strong to not only allow the Faithfulness assumption to be testable but also have the true causal skeleton (i.e., the adjacencies) be determined by data in the large sample limit. It is therefore worth investigating methods of causal discovery under this much weaker notion of faithfulness. As we already mentioned, this investigation is potentially useful even if the Faithfulness assumption is rarely exactly violated, for methods that do not rely on strong notions of faithfulness will probably be less errorprone at realistic sample sizes than those that do.
4 PC and GES against unfaithfulness and some more Robust algorithms
We are yet to work out a feasible method that is provably correct given only the TriangleFaithfulness assumption, but an examination of standard causal discovery algorithms with respect to violations of Faithfulness suggests some simple variations that, though not exactly correct given only the TriangleFaithfulness assumption, are better heuristic methods than the original. In this section, we focus on two wellknown algorithms, PC [15] and GES [1, 7].
4.1 PC and violations of faithfulness
As a result, the PC algorithm is very sensitive to a failure of AdjacencyFaithfulness or that of OrientationFaithfulness. For a violation of OrientationFaithfulness, Example 1 provides a simple illustration. In that example, if we feed an oracle of conditional independence derived from the distribution (or constructed based on statistical tests on a sufficiently large sample from the distribution) to the PC algorithm, the algorithm will output the triple \((V_1, V_2, V_4)\) as an unshielded collider, and similarly for the triple \((V_1, V_3, V_4)\).
Ramsey et al. [10] showed that such unreliability of PC in the presence of violations of OrientationFaithfulness can be remedied in principle, by using a more cautious or conservative step of inferring the orientation of unshielded triples. The idea is that given the correct skeleton, whether OrientationFaithfulness is true for an unshielded triple is testable, by checking more statements of conditional independence. The adjusted algorithm is named CPC (Conservative PC). We will discuss the conservative orientation step and its variations in more details in Sect. 4.3. For the moment, suffice it to say that it is a principled way to guard against violations of OrientationFaithfulness.
What about failures of AdjacencyFaithfulness? Here is a simplest example (that does not violate TriangleFaithfulness):
Example 2
Suppose \({\mathbf {V}} = \{V_1, V_2, V_3, V_4\}\), and the true causal structure is represented by the DAG in Fig. 2a. Suppose that the parameterization is such that the causal influence along the edge \(V_1\rightarrow V_4\) and that along the path \(V_1\rightarrow V_2\rightarrow V_3\rightarrow V_4\) balance out. As a result, in addition to the conditional independence relations entailed by the graph, the distribution satisfies one (and only one) extra independence: Open image in new window , which is a violation of AdjacencyFaithfulness. (Notice that TriangleFaithfulness trivially holds, as there is no triangle in the graph).
In this example, the PC algorithm, given an input of an oracle of conditional independence from the distribution, would output the pattern in Fig. 2b, while the true pattern is depicted in Fig. 2c. That is, the PC algorithm would miss the edge between \(V_1\) and \(V_4\), due to the extra, unfaithful independence (and consequently miss the collider).
Before we discuss how to mitigate this problem, it is worth noting that although an edge is missing from the skeleton returned by PC, the edges present in the PC skeleton are all correct. This is no accident. Spirtes and Zhang [14] observed that even if the AdjacencyFaithfulness fails, the SGS algorithm, a predecessor of PC, is provably asymptotically correct in this aspect: The adjacencies in the output are true, though nonadjacencies may be false. This is because the SGS algorithm searches through all subsets of \({\mathbf {V}}\backslash \{X, Y\}\) to look for a set that renders X and Y conditionally independent; if no such set is found, then the Markov assumption alone entails that X and Y are adjacent. Only the inference of nonadjacencies relies on the assumption of AdjacencyFaithfulness.
This argument does not straightforwardly apply to PC, as PC does not necessarily look at every subset. Still, no example is known to show that the PC algorithm, when supplied with a perfect oracle of conditional independence that does not respect AdjacencyFaithfulness, not only makes mistakes on nonadjacencies, but also may err about adjacencies. We suspect that there is no such example, but we are currently unable to find a proof. In any case, it is probably safe to think that in most cases, PC is asymptotically correct in its inference of adjacencies (as opposed to its inference of nonadjacencies), even if AdjacencyFaithfulness is violated.
Regarding the issue with the inferred nonadjacencies, Spirtes and Zhang [14] suggested a way to confirm some nonadjacencies as opposed to others by testing some forms of Markov condition in the output of (conservative) PC, which was elaborated and implemented by [4]. A limitation of this approach is that it only seeks to confirm some nonadjacencies, without attempting to fill in the missing edges. A natural, alternative idea is to search for a best way to add edges back to the PC/CPC output. For this purpose, the algorithm known as Greedy Equivalence Search (GES) may play a role.
4.2 GES and violations of faithfulness
For our present purpose, it is worth highlighting that in the forward phase of the algorithm, every time the search moves from a current pattern to a new one, the DAGs represented by the current pattern are proper independencesubmodels of the DAGs represented by the new one. Likewise, in the backward phase of the algorithm, every time the search moves from a current pattern to a new one, the DAGs represented by the new pattern are proper independencesubmodels of the DAGs represented by the current one. A score is said to be consistent if in the large sample limit, (1) any DAG (or its pattern) that is Markov to the underlying distribution has a higher score than any DAG (or its pattern) that is not Markov to the underlying distribution, (2) if two DAGs are both Markov to the underlying distribution and one is a proper independencesubmodel of the other, then the former has a higher score than the latter. For linear Gaussian models and multinomial models, for example, the Bayesian scores and BIC are all consistent [1].
It follows from the consistency of the scoring and the way GES moves in the search space that asymptotically the output of the algorithm satisfies the Markov and Pminimality conditions with the underlying distribution. Therefore, if the Faithfulness assumption holds, the GES algorithm is asymptotically correct, for given Faithfulness, the pattern of the true causal DAG is the unique pattern that satisfies the Markov and Pminimality conditions. What if the Faithfulness assumption does not hold? For the kind of unfaithfulness described in Example 2, the GES algorithm remains valid, for despite the unfaithfulness, the pattern in Fig. 2c remains the only pattern that satisfies the Markov and Pminimality conditions with the unfaithful distribution. We suspect that this is generally the case for violations of AdjacencyFaithfulness (that do not violate TriangleFaithfulness).
In contrast, failures of OrientationFaithfulness can easily lead GES astray. Take Example 1 for instance. As we already pointed out, in that case there are more than one pattern that satisfy the Markov and Pminimality conditions with the underlying distribution. As a result, there is little guarantee that the GES algorithm would end up with the true causal pattern, even in the large sample limit. For example, if we parameterize the graph in Fig. 1a as a linear structural equation model with Gaussian error terms and make the two pathways cancel as required by the example, then the GES algorithm will frequently return the pattern for the DAG in Fig. 1b, even at big sample sizes. Section 5.1 reports some simulation results to illustrate this point.
These considerations suggest that GES is probably more unreliable under failures of OrientationFaithfulness than it is under failures of AdjacencyFaithfulness. This motivates us to consider combining GES with a CPClike orientation step, to which we now turn.
4.3 Some heuristic hybrid algorithms
As already mentioned, the CPC algorithm [10] modifies PC’s rule of inferring unshielded colliders or noncolliders into a more cautious or conservative step. For an unshielded triple (X, Y, Z) in a skeleton, the CPC algorithm does not just consider one conditioning set that renders X and Z conditionally independent, but will check, roughly speaking, all subsets of the set of variables that are adjacent to X and of the set of variables that are adjacent to Z. If all sets that render X and Z conditionally independent exclude Y, then (X, Y, Z) is judged to be a collider. If all sets that render X and Z conditionally independent include Y, then (X, Y, Z) is judged to be a noncollider. If, however, some such sets exclude Y while the other such sets include Y, then (X, Y, Z) is marked to be an ambiguous triple (i.e., the judgment is suspended regarding whether the triple is a collider or a noncollider), for the result indicates that OrientationFaithfulness fails.
A more recent variation on the theme [2] uses a majority rule decision procedure as follows. As in CPC, this procedure first finds, for any unshielded triple (X, Y, Z) in a skeleton, all subsets of variables adjacent to X or of variables adjacent to Z that render X and Z conditionally independent. Then the triple is marked according to a majority rule: If Y is excluded from a majority of such sets, then the triple is marked as a collider; if Y is included in a majority of such sets, then the triple is marked as a noncollider; if, however, Y is excluded from (or included in) exactly half of such sets, then the triple is marked as ambiguous.
 (i)
if \(I(X, Y, Z)/(O(X, Y, Z)+I(X, Y, Z)) \le \alpha \), then mark (X, Y, Z) as a collider;
 (ii)
if \(I(X, Y, Z)/(O(X, Y, Z)+I(X, Y, Z)) \ge 1\alpha \), then mark (X, Y, Z) as a noncollider;
 (iii)
otherwise, mark (X, Y, Z) as ambiguous.

PC+GES: Run PC first and feed the output pattern to GES (and prohibit GES to take away any adjacency in the PC output).^{5}

GES+c: Run GES first, take the skeleton of the output and apply the \(\alpha \)conservative rule followed by Meek’s orientation rules.^{6}

PC+GES+c: Run PC+GES, take the skeleton of the output and apply the \(\alpha \)conservative rule followed by Meek’s orientation rules.
5 Simulations
We report two sets of simulation results. One is on the two toy examples mentioned previously, in which we try exact violations of OrientationFaithfulness and of AdjacencyFaithfulness, respectively. The results not only serve to support our earlier claims about the aforementioned algorithms, but also show that two other stateoftheart algorithms, the MaxMin Hill Climbing (MMHC) algorithm [16] and the PCstable algorithm [2], would stumble over one or both of the examples.
The other is from much more comprehensive experiments that follow a fairly standard setup, in which the probability of having exact violations of faithfulness is zero. These results indicate that the proposed algorithms are less errorprone at realistic sample sizes even when Faithfulness is not exactly violated. All experiments reported below are done on linear Gaussian models.
5.1 Toy examples with exact faithfulness violations
We first present some results on the two toy examples, in which it is easy to create exact violations of faithfulness. Recall that Example 1 in Sect. 3 involves a violation of OrientationFaithfulness, in which the true causal structure is represented by the DAG in Fig. 1a, and the two causal pathways, \(V_1\rightarrow V_2\rightarrow V_4\) and \(V_1\rightarrow V_3\rightarrow V_4\), are supposed to exactly cancel so that \(V_1\) and \(V_4\) are independent according to the true distribution. In our experiment, we randomly draw edge coefficients for the following three edges in Fig. 1a, \(V_1\rightarrow V_2\), \(V_2\rightarrow V_4\) and \(V_1\rightarrow V_3\), uniformly from \([1,0.1]\cup [0.1, 1]\), and then set the coefficient associated with \(V_3\rightarrow V_4\) as \(\beta _{12}\beta _{24}/\beta _{13}\), where \(\beta _{12}, \beta _{24}, \beta _{13}\) denote the edge coefficients for \(V_1\rightarrow V_2\), \(V_2\rightarrow V_4\), \(V_1\rightarrow V_3\), respectively. The variance of each error term in the model is drawn uniformly from [0.5, 1]. (Means are all set to 0.) We generate 100 linear structural equation models this way and draw from each model 50 i.i.d samples of size 5000. We use a big sample size for this experiment, for the errors we aim to reveal are not a matter of sample size.
On the \(100 \times 50\) datasets we run various algorithms. We use significance level of 0.01 in tests of conditional independence and use BIC in the GES algorithm. For this simulation, the value of \(\alpha \) in the conservative orientation does not seem to matter; all values of \(\alpha \) give essentially the same results.
As expected, PC and GES very frequently judge the triple \(V_1\rightarrow V_2\rightarrow V_4\) and the triple \(V_1\rightarrow V_3\rightarrow V_4\) to be colliders. In particular, as we predicted in Sect. 4.2, GES frequently—about 65% of the time—outputs the pattern for the DAG in Fig. 1b.
Just as CPC (and PCstable, which uses the majority rule for orientation and is reasonably robust against simple violations of OrientationFaithfulness like this one) can to a good extent avoid such errors of PC, GES+c helps to decrease such orientation errors of GES. In this simple example, GES+c in most cases mark the triple \(V_1\rightarrow V_2\rightarrow V_4\) and the triple \(V_1\rightarrow V_3\rightarrow V_4\) as ambiguous. Table 1 lists the average arrow precisions of the relevant algorithms in this case, where arrow precision is the percentage of true directed edges (i.e., directed edges that also appear in the true pattern) among all the directed edges in the estimated graph. (Since arrow precision is used here to measure how well mistaken inferences to arrows are avoided, we take arrow precision to be 1 when the estimated graph contains no directed edges.) Thanks to the conservative orientation, GES+c improves the arrow precision of GES (i.e., avoids some mistaken arrows in the GES output), as CPC does to PC.
Arrow precisions in the experiment on Example 1
Algorithm  Arrow precision 

GES  0.35 
GES+c  0.96 
PC  0.49 
PCstable  0.96 
CPC  0.99 
MMHC  0.56 
Example 2 in Sect. 4.1 is intended to illustrate a simple violation of AdjacencyFaithfulness: The true causal structure is taken to be the DAG in Fig. 2a, but the true distribution is such that \(V_1\) and \(V_4\) are independent (despite their adjacency). Again, we generate exact violations by randomly selecting three edge coefficients (associated with \(V_1\rightarrow V_2\), \(V_2\rightarrow V_3\), and \(V_3\rightarrow V_4\), respectively), and setting the fourth (associated with \(V_1\rightarrow V_4\)) to exactly balance the two directed paths from \(V_1\) to \(V_4\) in Fig. 2a. The setting is otherwise the same as in the previous example.
In 99% of the \(100 \times 50\) trials, the PC algorithm misses the adjacency between \(V_1\) and \(V_4\). The PCstable algorithm, which uses an orderindependent search of adjacencies and is usually more accurate than PC, does not help with this case. As we predicted, PC+GES is able to pick up the edge most of the time. Indeed, GES by itself outputs the true pattern most of the time (68% of the trials), which is consistent with our analysis in Sect. 4.2. MMHC, on the other hand, almost always misses the edge between \(V_1\) and \(V_4\), and outputs the pattern in Fig. 2b most of the time.
True adjacency rates and false adjacency rates in the experiment on Example 2
Algorithm  True adj. rate  False adj. rate 

PC  0.75  0.01 
PCstable  0.75  0.01 
PC+GES  0.95  0.02 
GES  0.93  0.06 
MMHC  0.76  0.02 
Ideally, we would like to run a more systematic experiment using perhaps random graphs and more complex faithfulness violations. Unfortunately, we have not yet thought of a way to automatically generate exact violations of AdjacencyFaithfulness (without being violations of TriangleFaithfulness) and/or of OrientationFaithfulness on a random graph. Still, the above toy simulations well illustrate the main points we made earlier.
5.2 Systematic simulations without exact faithfulness violations
Again, we use significance level of 0.01 in tests of conditional independence and use BIC in the GES algorithm. For the conservative orientation, we tried a number of values of \(\alpha \) in a smallscale experiment, all of which gave qualitatively similar results, but \(\alpha =0.4\) seemed to strike the best balance between being cautious and being informative (as measured by the F1 score we describe below). This finding is not necessarily generalizable,^{7} but provides some indication that \(\alpha =0.4\) works reasonably well in our setting. So we use \(\alpha =0.4\) in the present simulation.
For orientations, we first compare GES+c vs GES, and PC+GES vs PC+GES+c in terms of their arrow precisions as defined in Sect. 5.1, which suggest how good they are at avoiding false arrows. Figure 5 shows the average arrow precisions of the 4 algorithms in various settings. It is clear that GES+c significantly increases the arrow precision of GES in all settings, and likewise for PC+GES+c vs PC+GES. Figure 6 also presents a boxplot of the AP values for a fixed sample size (n = 1000), with similar implications.
However, arrow precision on its own does not mean much, as an extremely conservative orientation rule can achieve maximum precision or avoid all errors by always suspending judgment. So we also compare the standard F1 measure that combines arrow precision and arrow recall, where arrow recall is the number of true directed edges in the estimate graph (i.e., directed edges that also appear in the true pattern) divided by the number of directed edges in the true pattern,^{9} and \(\text {F}1 = 2(\text {Precision}\times \text {Recall})/(\text {Precision} + \text {Recall})\), with the standard stipulation that \(\text {F}1=0\) if \(\text {Precision}=\text {Recall}=0\). Figure 7 shows the F1 scores of the 4 algorithms in all settings. On this measure, GES+c clearly outperforms GES, except for the two settings with the lowest dimension, in which the two stay very close. So our conservative orientation rule does not improve precision by simply sacrificing informativeness. For PC+GES, the conservative orientation rule also improves the F1 score in sparse settings, while for dense settings, PC+GES and PC+GES+c have quite comparable F1 scores.
We suspect that the observed improvements are connected to the fact that the proposed algorithms are designed to better handle faithfulness violations, but we realize that it is premature to claim a definite connection. After all, other hybrid algorithms [3, 16] such as MMHC achieve better performance, without being designed to deal with exact violations of faithfulness as illustrated by the simple simulations in Sect. 5.1.^{10} Indeed, as shown in Fig. 8, the F1 scores of MMHC are fairly comparable to those of GES+c and PC+GES+c. Still, it is interesting to see that certain modifications motivated by theoretical concerns with unfaithfulness turn out to improve performance even when faithfulness is not exactly violated, and this seems to make it at least reasonable to hypothesize a connection.
6 Conclusion
We examined two representative causal discovery algorithms that normally assume the Faithfulness assumption, from the perspective of how robust they are against unfaithfulness. One of them is the GES algorithm, and it is the first time that a scorebased method is brought to bear on a line of inquiry that has thus far been confined to the constrainbased approach. Although our discussion was not yet fully rigorous or general, it yielded some insights that motivated a couple of simple hybrid algorithms, which proved to be worthy candidates in our simulation studies.
A component in our hybrid algorithms is a conservative orientation rule indexed by a parameter \(\alpha \) that generalizes the conservative rule in the CPC algorithm and the majority rule in the PCstable algorithm. An open question is how to choose the value of this parameter in a systematic and sophisticated way (which, as we indicated in Sect. 4.3, will probably be formulated as an optimization problem, with an objective function that measures the expected extent to which the outcome is well balanced between accuracy and informativeness). At this point, we can only report that in our simulations, \(\alpha =0.4\) appears to be a good choice. We hope to work out more convincing recommendations in future work.
As we argued in Sect. 3, there seem to be excellent reasons to explore feasible methods of causal discovery that are provably correct given only the TriangleFaithfulness assumption. In this regard, all the algorithms we considered in this paper are heuristic methods; none of them is provably correct given only the TriangleFaithfulness assumption. It is clear that the CPC orientation rule is provably correct given a correct skeleton, but it is unclear how to efficiently find the true skeleton given only the TriangleFaithfulness assumption, nor indeed is it clear that the skeleton is in principle determinable (unless our conjecture is proved to be true). These questions are both theoretically interesting and potentially beneficial to practice and are in our view worth studying further.
Footnotes
 1.
The distinction between a random variable and the vertex that represents it in a graph is as usual unimportant, and we will use “vertex” and “variable” interchangeably. We use boldface letters to denote sets of variables/vertices and italicized letters to denote individual variables/vertices.
 2.
 3.
In a single step, the algorithm considers all possible singleedge additions that can be made to all DAGs in the Markov equivalence class represented by the current pattern, scores all those valid patterns that result from such singleedge additions, and selects the best, if better than the current pattern. Thus, the algorithm is significantly less “greedy” than a greedy search over DAGs is.
 4.
We thank an anonymous referee for raising this interesting question.
 5.
PC+GES was studied empirically by [13] on discrete data. Their primary motivation was to improve the feasibility of GES.
 6.
Occasionally but very rarely, some unshielded triple (X, Y, Z) in the skeleton is such that no set is found to render X and Z conditionally independent. In this case, we add back an edge between X and Z. Similarly for PC+GES+c.
 7.
We thank an anonymous referee for emphasizing this point.
 8.
In our simulations, except in very rare situations, only the forward phase of the GES is needed in PC+GES.
 9.
For convenience, we stipulate that arrow recall is equal to 1 when the denominator is 0 (i.e., when the true pattern does not contain any directed edge). Obviously, when this stipulation applies, all algorithms will receive the same, maximal value on this measure, so the stipulation does not favor any algorithm.
 10.
We thank an anonymous referee for pressing this point.
Notes
Acknowledgements
We thank Kun Zhang for helpful discussions. JZ’s research was supported in part by the Research Grants Council of Hong Kong under the General Research Fund LU342213.
References
 1.Chickering, D.M.: Optimal structure identification with greedy search. J. Mach. Learn. Res. 3, 507–554 (2002)MathSciNetzbMATHGoogle Scholar
 2.Colombo, D., Maathuis, M.H.: Orderindependent constraintbased causal structure learning. J. Mach. Learn. Res. 15(1), 3741–3782 (2014)MathSciNetzbMATHGoogle Scholar
 3.Gasse, M., Aussem, A., Elghazel, H.: An experimental comparison of hybrid algorithms for bayesian network structure learning. In: Flach, P.A., Bie, T.D., Cristianini, N. (eds.) Machine Learning and Knowledge Discovery in Databases–European Conference, ECML PKDD 2012, Bristol, UK, September 2428, 2012. Proceedings, Part I, Springer, Lecture Notes in Computer Science, vol. 7523, pp. 58–73 (2012), doi: 10.1007/9783642334603 Google Scholar
 4.Havrilla, N.: Exploring very conservative search algorithms. Master’s thesis, Carnegie Mellon University, Pittsburgh (2015)Google Scholar
 5.Hoyer, P.O., Janzing, D., Mooij, J.M., Peters, J., Schölkopf, B.: Nonlinear causal discovery with additive noise models. In: Koller, D., Schuurmans, D., Bengio, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 21, Proceedings of the TwentySecond Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 8–11, 2008, Curran Associates, Inc., pp. 689–696 (2008)Google Scholar
 6.Meek, C.: Causal inference and causal explanation with background knowledge. In: Besnard, P., Hanks, S. (eds.) UAI ’95: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, Canada, August 18–20, 1995, Morgan Kaufmann, pp. 403–410 (1995)Google Scholar
 7.Meek, C.: Graphical causal models: selecting causal and statistical models. PhD thesis, Carnegie Mellon University, Department of Philosophy (1996)Google Scholar
 8.Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, Burlington (1988)zbMATHGoogle Scholar
 9.Pearl, J.: Causality: Models, Reasoning, and Inference, 2nd edn. Cambridge University Press, Cambridge (2009)CrossRefGoogle Scholar
 10.Ramsey, J., Zhang, J., Spirtes, P.: Adjacencyfaithfulness and conservative causal inference. In: UAI ’06, Proceedings of the 22nd Conference in Uncertainty in Artificial Intelligence, AUAI Press, Cambridge, MA, USA, July 13–16 (2006)Google Scholar
 11.Reichenbach, H.: The Direction of Time. University of Los Angeles Press, Los Angeles, CA (1956)Google Scholar
 12.Shimizu, S., Hoyer, P.O., Hyvärinen, A., Kerminen, A.J.: A linear nongaussian acyclic model for causal discovery. J. Mach. Learn. Res. 7, 2003–2030 (2006)MathSciNetzbMATHGoogle Scholar
 13.Spirtes, P., Meek, C.: Learning bayesian networks with discrete variables from data. In: Fayyad, U.M., Uthurusamy, R. (eds.) Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD95), Montreal, Canada, August 20–21, 1995, AAAI Press, pp. 294–299 (1995)Google Scholar
 14.Spirtes, P., Zhang, J.: A uniformly consistent estimator of causal effects under the ktrianglefaithfulness assumption. Stat. Sci. 29(4), 662–678 (2014)MathSciNetCrossRefGoogle Scholar
 15.Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search. MIT press, Cambridge (2000)zbMATHGoogle Scholar
 16.Tsamardinos, I., Brown, L.E., Aliferis, C.F.: The maxmin hillclimbing bayesian network structure learning algorithm. Mach. Learn. 65(1), 31–78 (2006)CrossRefGoogle Scholar
 17.Uhler, C., Raskutti, G., Bühlmann, P., Yu, B.: Geometry of faithfulness assumption in causal inference. Ann. Stat. 41, 436–463 (2012)MathSciNetCrossRefGoogle Scholar
 18.Verma, T., Pearl, J.: Equivalence and synthesis of causal models. In: Bonissone, P.P., Henrion, M., Kanal, L.N., Lemmer, J.F. (eds.) UAI ’90: Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence, MIT, Cambridge, MA, USA, July 27–29, 1990, Elsevier, pp. 255–270 (1990)Google Scholar
 19.Zhang, J.: A comparison of three occam’s razor for markovian causal models. Br. J. Philos. Sci. 64(2), 423–448 (2013)MathSciNetCrossRefGoogle Scholar
 20.Zhang, J., Spirtes, P.: Detection of unfaithfulness and robust causal inference. Minds Mach. 18(2), 239–271 (2008). doi: 10.1007/s1102300890964 CrossRefGoogle Scholar
 21.Zhang, J., Spirtes, P.: Intervention, determinism, and the causal minimality condition. Synthese 182, 335–347 (2011)MathSciNetCrossRefGoogle Scholar
 22.Zhang, K., Hyvärinen, A.: On the identifiability of the postnonlinear causal model. In: Bilmes, J.A., Ng, A.Y. (eds.) UAI 2009, Proceedings of the TwentyFifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18–21, 2009, AUAI Press, pp. 647–655 (2009)Google Scholar