causal
Analyze cause-and-effect relationships in the Semantica knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.
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/semantica:causal
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.
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- semantica-agi/semantica
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