Causal discovery algorithms take as their input facts about correlations among a set of observed variables, and they return as their output causal structures that can account for the correlations. We show that any causal explanation of Bell-inequality-violating correlations must contradict a core principle of these algorithms, namely, that an observed statistical independence between variables should not be explained by fine-tuning of the causal parameters. The fine-tuning criticism applies to all of the standard attempts at causal explanations of Bell correlations, such as superluminal causal influences, superdeterminism, and retrocausation. Nonetheless, we argue that by casting quantum theory as a theory of Bayesian inference, we can generalize the notion of a causal model and salvage a causal explanation of Bell correlations without fine-tuning.
Based on arXiv:1208.4119.