Identifying Causal Effects Using a Single Proxy Variable
Unobserved confounding is a key challenge when estimating causal effects from a treatment on an outcome. In this work, we assume that we observe a single, potentially multi-dimensional proxy variable of the unobserved confounder and that we know the mechanism that generates the proxy from the confounder. Under an assumption called Single Proxy Identifiability of Causal Effects or simply SPICE, we prove that this error mechanism is complete and causal effects are identifiable. We extend the proxy-based causal identifiability results by Kuroki and Pearl (2014); Pearl (2010) to multi-dimensional continuous settings, more flexible functional relationships and a broader class of distributions....
| Authors | Silvan Vollmer, Niklas Pfister, Sebastian Weichwald |
|---|---|
| Primary category | stat.ML |
| Published | 10 April 2026 |
| Last updated at source | 25 September 2026 |
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