Causal Attribution for Agentic Decisions: Estimators, Coupling, and a Traceability Specification
A provider of a high-risk AI system must keep records that make a decision traceable, and for agentic systems it has not been established what those records must contain for post-hoc causal attribution to be possible. We give the estimator framework and then the conditions under which it fails. We separate the marginal total effect that prior work measures from a common-random-numbers total effect that isolates a step's own contribution, add the natural direct effect under a pinned downstream, and check the estimators against hand derivations. Both estimands then fail, in the same direction. Under the marginal estimand a causally inert step has the identical total effect to the decisive o...
| Authors | Ajay Pravin Mahale |
|---|---|
| Primary category | cs.AI |
| Published | 6 September 2026 |
| Last updated at source | 6 September 2026 |
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