We introduce a novel framework that seamlessly integrates amortized Bayesian inference and active data acquisition, featuring adaptive strategies that can optimize for diverse, user-specified learning objectives at deployment.

We demonstrate that the PFN-framework allows for the accurate estimation of causal effects under weakened assumptions.