Causal Inference with Unstructured Outcomes
Causal inference has traditionally centered on scalar outcomes: whether a patient recovers, how much a worker earns, or how many visits a website receives. Modern studies increasingly ask causal questions about outcomes with richer form, such as clinical notes, open-ended survey responses, and images. A hospital may want to know how an AI documentation tool changes the notes physicians write, or how a nurse training program alters what patients say in survey responses. For such outcomes, the usual average treatment effect is ill-defined: one cannot meaningfully subtract one text or image from another. To this end, we propose a causal query for unstructured outcomes. The key idea is to lea...
| Authors | Kevin Christian Wibisono, Yixin Wang |
|---|---|
| Primary category | stat.ML |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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