On the potential influence of quantum noise on measuring effectiveness in clinical trials
To test the effectiveness of a drug, the medical researcher can instruct two randomly selected groups of patients to take the drug or not to take it. Each group should comply with the instructions exactly or else the causal effect cannot be identified satisfactorily. This holds true even when those who do not comply are identified afterwards, since latent factors such as patient's personality can influence both his decision and his physical response. However, one can still give bounds on the effectiveness of the drug depending on the rate of compliance. They are described by 16 inequalities. Remarkably, the proofs for these bounds given in the literature rely on models that represent all relevant latent factors influencing patient's behavior by hidden classical variables. In a strong analogy to the violation of Bell's inequality, half of the inequalities fail if patient behavior is influenced by latent quantum processes (e.g. in his nervous system). Quantum effects could fake an increase in the recovery rate by about 13%, although the drug would hurt as many patients as it would help if everyone took it. We show that the other eight inequalities remain true even in the quantum case. We do not present a realistic model showing the above effect. We only point out that the physics of decision-making could be relevant for the causal interpretation of every-day life statistical data. Our derivation of the remaining eight inequalities suggests how to avoid problematic hidden-variable models in classical causal reasoning by representing all latent factors by quantum systems.
bell's inequality physics of decision-making average causal effect hidden variable models noncompliance in clinical trials