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When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments
AI-generated covariates from notes, conversations, images, and wearable streams can change the causal question when their roles are left unspecified. A generated feature may represent a treatment version, pre-action state, history, design variable, mediator, outcome proxy, observation process, or intercurrent event; these roles are not interchangeable. We formulate a causal type discipline for sequential experiments: a versioned representation map, a causal role classifier, a claim-status filter, and an estimand lock. The lock fixes a standardized proximal effect before generated covariates en
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T19:24:40.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.