Experimentally adjudicating between different causal accounts of Bell inequality violations via statistical model selection
Patrick J. Daley, Kevin J. Resch, Robert W. Spekkens

TL;DR
This paper compares classical and quantum causal models explaining Bell inequality violations using statistical model selection, finding quantum models better predict experimental data and challenging superdeterminism assumptions.
Contribution
It introduces a method to empirically distinguish between classical and quantum causal accounts of Bell violations through predictive power analysis.
Findings
Quantum causal models outperform classical models in prediction accuracy.
Classical models are prone to overfitting, misinterpreting statistical fluctuations.
The approach allows testing superdeterminist models experimentally.
Abstract
Bell inequalities follow from a set of seemingly natural assumptions about how to provide a causal model of a Bell experiment. In the face of their violation, two types of causal models that modify some of these assumptions have been proposed: (i) those that are parametrically conservative and structurally radical, such as models where the parameters are conditional probability distributions (termed 'classical causal models') but where one posits inter-lab causal influences or superdeterminism, and (ii) those that are parametrically radical and structurally conservative, such as models where the labs are taken to be connected only by a common cause but where conditional probabilities are replaced by conditional density operators (these are termed 'quantum causal models'). We here seek to adjudicate between these alternatives based on their predictive power. The data from a Bell…
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