# Causal Inference **Entity class:** Causal And Behavioral-Modeling Layer concept **Causal Inference** is a concept in the causal and behavioral-modeling of the counterterrorism predictive-graph ontology. Causal Inference matters because the analytic object is a changing, partially observed system rather than a finished dossier. ## Counterterrorism predictive-graph role The page records capability and analytic function. Any claim of a named operational deployment remains subject to the [[wiki/Counterterrorism Predictive Graph Evidence Ladder|evidence ladder]]. The analytic state should distinguish ground truth, observed evidence, inferred state, and predicted state. ## Relationships - **domain router:** [[wiki/Causal Inference|Causal Inference]]. - **ontology neighbors:** [[wiki/Early Warning|Early Warning]] and [[wiki/Causal Graph|Causal Graph]]. - **synthesis:** [[wiki/Counterterrorism Predictive Graph|Counterterrorism Predictive Graph]] and [[wiki/Predictive Intelligence Loop|Predictive Intelligence Loop]]. - **machine-epistemology route:** [[wiki/Cross-Ontology Causal Attribution|Cross-Ontology Causal Attribution]], [[wiki/Epistemic Invariance|Epistemic Invariance]] and [[wiki/Universal Epistemic Engine|Universal Epistemic Engine]]. ## Conversational-AI Context [[articles/Epistemic Invariance in Conversational AI|Epistemic Invariance in Conversational AI]] applies causal inference to conversational claims by separating reported observation from interpretation, alleged agent and causal mechanism, then requiring evidence that connects the mechanism to the particular event. ## Simple Reminders, Quotations, and Thoughts > “A conversational AI fails the test of epistemic invariance when the same evidence is judged by a different standard of proof merely because the alleged causal agent changes—for example, when the system gives vastly different answers depending on whether the proposed agent is God, a government, or an artificial intelligence.” > **— Bryant McGill**, *Epistemic Invariance in Conversational AI, 2026* [[reminders/Scientific Literacy/Changing the Alleged Agent Must Not Change the Evidence by Bryant McGill|Changing the Alleged Agent Must Not Change the Evidence by Bryant McGill]] ## Sources - [NIST/SEMATECH — e-Handbook of Statistical Methods (accessed September 23, 2026)](https://www.itl.nist.gov/div898/handbook/) - [Oden Institute — Tan Bui-Thanh research profile (accessed September 23, 2026)](https://oden.utexas.edu/people/directory/Tan-Bui/) - [[articles/Epistemic Invariance in Conversational AI|Epistemic Invariance in Conversational AI]] — conversational causal-attribution and invariance test. **As of:** 2026-09-23