Standard causal discovery postulates that structure can be described by a directed acyclic graph (DAG), which can be partially reconstructed by using the faithfulness principle. According to this principle, conditional independencies in the observational distribution correspond exactly to those encoded in the unknown DAG. However, such algorithms are challenged by the fact that, without even stronger assumptions, approximate failures of faithfulness are empirically undistinguishable from exact ones. This leaves an inferential gap which can lead to major implications to causal effect identification. We introduce a different perspective, not reliant on exact independencies. By considering the specialized problem of discovering whether unmeasured confounding can be ruled out for a particular cause-effect pair, we introduce a principle we call naturalness, in which the strength of observational associations bounds the strength of causal dependencies. The bounding function is unknown, with degrees of freedom not present in faithfulness. We model uncertainty about this function using a Bayesian framing which allows for the use of experiments from separate studies, grounding an adaptive causal discovery principle on historical experimental data. We demonstrate its usefulness in partial identification of causal effects, and discuss how the promising results can motivate new principles for general causal discovery.

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