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Regardless of its size, observational data can usually only measure correlation, not causality.
It can be tricky to distinguish causal effects from mere correlation. What’s the solution? Experiments. These are the gold standard for measuring causality.
Regulation must create a sale opportunity.
WHY IS DATA XL DIFFERENT
We imitate real life. In the MIFID survey, we will present products that do not exist but that seem believable to consumers. As people choose these products and participate in this experiment, they will reveal their preference mechanisms.
We use economics’ revealed preference theory. This theory holds that consumers’ preferences can be revealed by what they purchase under different circumstances.
We do not need to measure client rationality to obtain good predictions. We only need consistent behaviour (within client groups/clusters).
What’s the solution to finding consistency and causality? Experiments. These are the gold standard for measuring causality. (Ideally, experiments would be carried out continuously so we can assess subtle environmental cues through cluster and discriminant analyses.)