Ignoring the exponential blowup, one could have a prediction market over all the causal models to elicit the best one (including an option “all of these are wrong/important variables are missing”).
[on reflection, this seems hard unless you commit to doing a bunch of experiments or otherwise have a way to get the right outcome]
Then, with a presumptively trustworthy causal model, the “make adjustments to observational data” approach would be more reliable to estimate from other markets.
However, it feels like it could be the case that trying to do both of these things at once might screw up the incentives—in other cases there are sometimes impossibility results like this.
“How can we design mechanisms to elicit causal information, not just distributional properties” seems like a really interesting question that seemingly hasn’t received much attention.
Ignoring the exponential blowup, one could have a prediction market over all the causal models to elicit the best one (including an option “all of these are wrong/important variables are missing”).
[on reflection, this seems hard unless you commit to doing a bunch of experiments or otherwise have a way to get the right outcome]
Then, with a presumptively trustworthy causal model, the “make adjustments to observational data” approach would be more reliable to estimate from other markets.
However, it feels like it could be the case that trying to do both of these things at once might screw up the incentives—in other cases there are sometimes impossibility results like this.
“How can we design mechanisms to elicit causal information, not just distributional properties” seems like a really interesting question that seemingly hasn’t received much attention.