ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal inference instead uses proxy variables to identify effects under hidden confounding. However, nonparametric proximal estimation can be challenging in practice: recovering causal estimands such as the conditional average treatment effect (CATE) requires solving an ill-posed integral equation that is data-hungry, hyperparameter-sensitive, and optimization-unstable. Bayesian inference for such models provides a desirable alternative, mitigating these difficulties by regularizing through the prior. However, computing a posterior is itself challen...
| Authors | Christophe Muller, Ayub Kharel, Alex Luedtke et al. |
|---|---|
| Primary category | stat.ML |
| Published | 6 October 2026 |
| Last updated at source | 13 October 2026 |
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