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Analyze cause-and-effect relationships in the VeritasReason knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.
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Based on SOC occupation classification
VeritasReason full-stack knowledge graph skill for context graphs, decision intelligence, explainability, extraction, reasoning, visualization, ontology, provenance, policy, and export workflows.
Track and inspect graph changes, diffs, temporal updates, and the impact of new data on VeritasReason knowledge graphs.
Full decision lifecycle in VeritasReason � record, query, find precedents (hybrid/advanced), analyze influence, explain, insights dashboard, list, and record exceptions. Uses AgentContext, ContextGraph, DecisionQuery, CausalChainAnalyzer, DecisionRecorder.
Detect duplicate entities, duplicate groups, and relationship duplicates in VeritasReason using fuzzy matching, schema heuristics, and graph similarity.
Generate, inspect, and use node/text embeddings in VeritasReason — compute Node2Vec embeddings, find similar nodes, score link predictions, batch similarity, and pairwise similarity. Uses NodeEmbedder, SimilarityCalculator, LinkPredictor, and AgentContext. Sub-commands: compute, similar, similarity, predict-link, top-links, batch, pairwise.
Explain VeritasReason reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale.
| name | causal |
| description | Analyze cause-and-effect relationships in the VeritasReason knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores. |
Analyze causal relationships and infer impacts. Usage: /veritasreason: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 veritasreason.context.causal_analyzer import CausalChainAnalyzer
from veritasreason.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.