Observationally Informed Adaptive Causal Experimental Design

Abstract

Randomized Controlled Trials (RCTs) represent the gold standard for causal inference yet remain a scarce resource. While large-scale observational data is often available, it is typically used only for retrospective fusion, and remains discarded in prospective trial design due to bias concerns. We argue that this ''tabula rasa'' data acquisition strategy is inefficient when observational models contain useful structural information. In this work, we propose Active Residual Learning, a new paradigm that leverages the observational model as an informative but biased prior. This shifts the experimental focus from learning target causal quantities from scratch to estimating residual corrections ..