Consider a conditional prediction market, e.g. "if my cool policy is implemented, then widget production will increase by at least 15%". To my understanding, markets like this are intended as a tool for finding and the market just gets unwound or undone or refunded if doesn't occur.
I can work through the math and see that refunding the market indeed makes the price reflect , but this exacerbates one of the biggest issues with prediction markets: no one wants to lock up of capital to extract of profit in a year, so no one will lock up of capital to extract of profit in a year and only if some extra event happens.
My question is: are there any interesting or viable alternative ways to run a counterfactual or conditional prediction market? Off the top of my head, I could imagine using markets for and to derive , which would still pay out something if didn't occur.
I don't think the capital being locked up is such a big issue. You can just invest everyone's money in bonds, and then pay the winner their normal return multiplied by the return of the bonds.
A bigger issue is that you seem to only be describing conditional prediction markets, rather than ones that truly estimate causal quantities, like P(outcome|do(event)). To see this, note that the economy will go down IF Biden is elected, whereas it is not decreased much by causing Biden to be elected. The issue is that economic performance causes Biden to be unpopular to a much greater extent than Biden shapes the economy. To eliminate confounders, you need to randomiser the action (the choice of president), or deploy careful causal identification startegies (such as careful regression discontinuity analysis, or controlling for certain variables, given knowledge of the causal structure of the data generating process). I discuss this a little more here.