// Analyze cause-and-effect relationships in the Semantica knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.
Semantica full-stack knowledge graph skill for context graphs, decision intelligence, explainability, extraction, reasoning, visualization, ontology, provenance, policy, and export workflows.
Explain Semantica reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale.
Detect duplicate entities, duplicate groups, and relationship duplicates in Semantica using fuzzy matching, schema heuristics, and graph similarity.
Export Semantica graphs, results, and provenance to JSON, RDF, Parquet, CSV, GraphML, and other formats.
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Track and inspect graph changes, diffs, temporal updates, and the impact of new data on Semantica knowledge graphs.
| name | causal |
| description | Analyze cause-and-effect relationships in the Semantica knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores. |
Analyze causal relationships and infer impacts. Usage: /semantica:causal <task> [args]
$ARGUMENTS = task + optional target entity, filter, or intervention.
chain [--subject <node>] [--depth N]Build and inspect causal chains for a subject or category.
from semantica.context.causal_analyzer import CausalChainAnalyzer
from semantica.context import AgentContext
# Option 1: Use an existing AgentContext decision backend
chain = ctx.get_causal_chain(
decision_id=decision_id,
direction="upstream",
max_depth=depth,
)
# Option 2: Use CausalChainAnalyzer directly
analyzer = CausalChainAnalyzer(graph_store=ctx.knowledge_graph)
downstream = analyzer.get_causal_chain(
decision_id=decision_id,
direction="downstream",
max_depth=depth,
)
Output: chain steps, cause strength, effect reach, and summary graph.
intervene <node> <action> [--scenario <json>]Analyze decision impact and influenced decisions (current causal API).
analyzer = CausalChainAnalyzer(graph_store=ctx.knowledge_graph)
impact_score = analyzer.get_causal_impact_score(decision_id=decision_id)
influenced = analyzer.get_influenced_decisions(
decision_id=decision_id,
max_depth=depth,
)
Return: impact score, influenced decisions, and downstream scope.
counterfactual <fact> [--weight N]Trace root causes and temporal causal paths.
analyzer = CausalChainAnalyzer(graph_store=ctx.knowledge_graph)
roots = analyzer.find_root_causes(decision_id=decision_id, max_depth=depth)
historical_chain = analyzer.trace_at_time(
event_id=decision_id,
at_time="2026-01-01T00:00:00Z",
direction="upstream",
max_depth=depth,
)
Output: root decision lineage and time-bounded causal context.