Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods for causal sensitivity analysis are per-instance procedures, meaning that changes to the dataset, causal query, sensitivity level, or treatment require new computation. Here, we instead present an in-context learning approach. Specifically, we propose an amortized approach to causal sensitivity analysis based on prior-data fitted networks. A key challenge is that the sensitivity bounds are not directly available when sampling training data. To address this, we develop a general prior-data construction that is applicable across the class of gen...
| Authors | Emil Javurek, Dennis Frauen, Marie Brockschmidt et al. |
|---|---|
| Primary category | stat.ML |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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