Extending the fair sampling assumption using causal diagrams

Discarding undesirable measurement results in Bell experiments opens the detection loophole that prevents a conclusive demonstration of nonlocality. As closing represents major technical challenge for many practical experiments, it is customary to assume so-called fair sampling assumption (FSA) that, its original form, states collectively postselected statistics are sample ideal statistics. Here, we analyze FSA from viewpoint causal inference: We derive structure must be present any model faithfully encapsulates FSA. This provides an easy, intuitive, and unifying approach includes different accepted forms underlines what really assumed when using then show can not only applied scenarios with non-ideal detectors or transmission losses, but also where parts correlations postselected, e.g., particles' destinations superposition state. Finally, demonstrate applicable multipartite test (genuine)