causal

name	causal
description	Analyze cause-and-effect relationships in the VeritasReason knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.

/veritasreason:causal

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.