The SoCK Ontology (Synthesis of Causal Knowledge)
This ontology is created to support the integration and synthesis of causal knowledge from various sources, including causal discovery algorithms, expert knowledge, or other sources of causal discovery. It covers three main focus areas: "Causal Graph and Metrics" focuses on the representation of a specific causal graph, its edges, and comparison metrics, where discrepancies between two graphs can be represented. "Causal Discovery Method" focuses on the methods applied to derived a causal graph. This could be a causal discovery algorithm, including the hyper parameter settings that were applied in a specific run to create a specific causal graph. "Data and Context" focuses on the data that was investigated to derive a causal graph. This includes the variables of a dataset, which were investigated, as well as assumptions that are posed on a dataset by an algorithm/method.
A causal discovery algorithm applies a set of conditional independence tests.
relation from causal edge to the causal graph that is defining this specific edge.
relation from a graph comparison metric to the two graph entities that are compared by the causal graph.
A relation that defines the set of variables to condition on, to define a separation statement. For (A _||_B |C), the "conditionedOn" variable would be C.
a connection relation from a causal edge to the corresponding causal nodes
A relation between a datasource and its variables, to define which variables are contained in a datasource.
A causal graph contains a set of causal nodes. Even if there is no edge defined that is connected to this causal node, it can still be part of the causal graph, as it was considered in the causal discovery method.
A datasource can fulfill a certain assumption that is posed by a causal discovery method.
A connection from a causal edge to a causal node, which signified a "cause" relation. For an Edge A -> B, a causal edge would be connected to node A via a "hasCause" relation, and it would be connected to node B via a "hasEffect" relation.
A connection from a causal edge to a causal node, which signified an ambiguous causal relation. This means, that for all Markov-equivalent causal graph, there are some causal relations A->B, and some causal relations A <- B. For an Edge A°-°B, a causal edge would be connected to both nodes (A, B) via a "hasCauseOrEffect" relation.
A connection from a causal edge to a causal node, which signified an "effect" relation. For an Edge A <-> B, a causal edge would be connected to both nodes A and B via a "hasEffect" relation.
A relation that defines the set of independent variables define in a separation statement. For (A _||_B |C), the "hasIndependentVariable" variable would be A.
Relation between an implied separation, defined by a causal graph, and a conditional independence test, which is performed on data by a causal discovery algorithm. Ideally, these two should imply the same separations.
A causal graph implies a set of implied separations. which represent a separation of two variables, when conditioned on a set of variables.
A relation that defines the set of variables the independent variable is independent of, as defined in a separation statement. For (A _||_B |C), the "independentOf" variable would be B.
A marginalized graph can be reverted to a bigger graph, based on the encoded knowledge. However, this does not always revert the causal graph to its original graph, as some information was lost via the initial marginalization.
A causal graph can be marginalized from a bigger causal graph, by excluding some causal nodes in the consideration of causal edges. This is not necessarily a subgraph of the original causal graph.
A relation to connect a causal discovery method to the causal graph is produces.
An implied separation represents a separation of two variables, which are conditioned on a set of variables. The exact variables in a separation are defined by Separation, while ImpliedSeparation only refers to the Separation it implies.
A relation between a graphSummaryMetric and its corresponding causal graph, which summarizes its global features.
A relation between a conditional independence test, and the implied separation that is tested by it.
A conditional indepdence test test a separation of two variables, when conditioned on a set of other variables. this setup is defined by the class :Separation.
A relation between a causal discovery method and the data source the method investigates.
Each causal discovery method works under a set of assumptions. This is predefined by the specific method and how it operates, and cannot be adapted for each run of an algorithm.
The absolute distance value between two graphs. This is the amount of differences in total.
The relative distance value between two graphs. This is the amount of differences in total divided by the total amount of possible relations.
The function call of a specific package that implements a causal discovery algorithm, specifying all of its parameters.
Defines the Datatype of a variable (boolean, continuous, nominal)
The p value of the conditional independence test. This is represents the significance of a tested separation.
The programming language, in which the method was run.
The package or library, referring to a concrete implementation of the method, that was used to run a certain algorithm
An ADMG is a more complex graph used when we suspect there are unmeasured (latent) variables. It contains two types of edges: directed arrows ($\to$) and bidirected arrows ($\leftrightarrow$).
A subclass of a causal discovery method, which uses some kind of AI model to produce a causal graph.
A causal discovery algorithm that can work on IID (Independent and Identically Distributed) data.
An assumption that is taken by a causal discovery method. This assumption must be fulfilled for a method to produce valid results.
Completed Partially Directed Acyclic Graph (CPDAG)
A causal discovery algorithm that uses some kind of statistical methods to derive a causal graph.
A Method for causal discovery, which produces a causal graph, usually based on a datasource, but not necessarily.
An edge between two causal nodes, which defines some kind of causal relation between the two nodes.
A graph showing causal relations between a set of nodes, which was produced by some causal discovery algorithm.
Causal Sufficiency states that all confounders of the observed variables have been measured and are included in the data.
Conditional Independence refers to the situation where a variable (A) is independent of another set of variables (B) given (conditional on) a specific set of variables (C). This means that knowing one set of variables (B) provides no additional information about the variable in question (A) when the other set is already known (C).
A DAG consists of nodes and directed edges (arrows) where no path exists that starts and ends at the same node.
A DG(directed graph) is a set of nodes connected by directed edges.
A unique identifier for a dataset that is used for causal discovery.
a single graph that represents an entire Markov equivalence class, using directed edges for relationships where the causality is logically certain and undirected edges for those where the direction remains statistically indistinguishable.
The faithfulness assumption in causal inference states that all statistical independencies observed in data truly reflect conditional independencies (d-separations) in the underlying causal graph, meaning no effects perfectly cancel out due to specific parameter settings.
A metric that analyses graph properties, summarizing their structure.
Hamming distance for graphs measures structural difference, typically by comparing their binary adjacency matrices.
A human (expert), whose expertise is used to produce a causal graph.
An independent and identically distributed datasource.
An implied separation determines if two sets of variables are statistically independent based on the paths and arrowheads connecting them in a causal graph (which implies this separation).
An LLM, which was used to produce a causal graph.
A node in a causal graph, which contains unobserved variables.
linearity assumption means the treatment's effect on the outcome is a constant, straight-line change.
An implied Separation for timeseries data.
NOTEARS (Non-combinatorial Optimization for Learning Interpretable Acyclic Graphs) is a causal discovery algorithm, which treats the causal discovery problem as a continuous optimization problem.
A causal discovery algorithm to identify the topological ordering of variables in a Directed Acyclic Graph (DAG) by leveraging the statistical properties of the coefficient of determination (R2).
A superclass, which encompasses Independence Separations, such as Conditional Independence and Implied Separations.
Separation distance measures how different two causal graphs are by focusing on their conditional independence (separation) properties.
Conditional Indepdendence of Temporal Variables are slightly different to Conditional Independence of IID Variables, as this is not a symmetric relation anymore, given time. Thus, temporal conditional independence always requires a direction.
A timeseries datasource, meaning that measurements are associated with timestamps. The present of a variable is dependent on its past observations.
A Variable, defined as part of a data set.
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