A causal graph is a directed graph model where nodes represent variables and directed edges encode direct causal influences between them. Its parameters are: (1) a set of nodes V mapping to variables, (2) a set of directed edges E where (X,Y) ∈ E means X has a direct causal effect on Y, and (3) an acyclicity constraint — no directed cycles — ensuring temporal consistency of causal ordering. The graph persists as a formal model encoded in data structures (adjacency matrices, edge lists) and transmitted through scientific literature, software libraries (e.g., DoWhy, pgmpy, causalgraphicalmodels), and computational frameworks that perform causal discovery and inference. The structure enables querying counterfactuals, identifying confounders via d-separation, and computing do-calculus interventions.\n\n[formal: causal_graph | substrate: mind | horizon: a life | explicit: yes | epoch: 0.01]
Accepted ontology entry
causal graph
A causal graph is a directed graph model where nodes represent variables and directed edges encode direct causal influences between them. Its parameters are: (1) a set of nodes V mapping to variables, (2) a set of directed edges E where (X…
Definition
Why it is in scope
A human-made directed graph that represents causal relationships between variables: nodes stand for variables and directed edges encode direct causal influences. Built to persist as a formal model used in causal inference, epidemiology, and machine learning for reasoning about interventions and counterfactuals.
Names and aliases
- causal graphen · CANONICAL
Relations from this entry
- cmrwtg97a01absoac34g3i9jmINSTANCE_OF →
A causal graph IS a specific kind of graph — a directed graph where nodes represent variables and edges represent causal relationships. A competent speaker would call a causal graph a graph. Nearest kind confirmed as graph.
- cms7qpssn006q1yrmdbysknhdSERVES →
A causal graph IS BUILT for the sake of causal inference — its designed purpose is to represent causal relationships so that causal inference (identifying cause-effect from data) can be performed. The servant (graph) points at the master (inference). Law 8d.
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Record identity
- Created
- Aug 8, 2026, 12:55 PM UTC
- Content hash
- 418e709c0e6e304a84ce78ea5d5027611836e6d0eb5f3795aba7a47e2ea3e029