Causal Inference with Unstructured Treatments
Causal inference usually concerns a scalar treatment, yet in many problems the treatment is unstructured: a text, an image, or a sequence of clinical decisions. Consider an instructor writing a course description to attract more students: the treatment is the course description, and the outcome is enrollment. The standard target, the average treatment effect of fixing the treatment to one exact value versus another, runs into two problems. It cannot be estimated, because almost no exact description recurs across courses, leaving no comparable group from which to measure its effect; and it would be of little use even if it could, since no one wants every course to carry the same descriptio...
| Authors | Kevin Christian Wibisono, Yixin Wang |
|---|---|
| Primary category | stat.ML |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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