This research introduces a batch-adaptive annotation framework that helps extract and measure causal effects from unstructured text documentation in healthcare and social services.

• Converts complex text data (clinical notes, case records) into measurable outcomes for causal inference
• Employs a novel batch-adaptive approach that optimizes human annotation resources
• Addresses the critical challenge of measuring intervention effectiveness from narrative documentation
• Demonstrates practical applications in healthcare and social services settings

For medical professionals, this technology represents a breakthrough in utilizing existing clinical documentation to measure treatment effectiveness without additional data collection. The framework transforms qualitative notes into quantifiable outcomes that can inform evidence-based practice.

Batch-Adaptive Annotations for Causal Inference with Complex-Embedded Outcomes