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Causal prediction markets.
Prediction markets (and prediction tournaments more generally) may be useful for telling us not only what will happen, but which actions will achieve our goals. One proposal for getting prediction markets to help with this is to get users to make conditional predictions. For example, we can ask the question "if Biden wins the election, GDP will be higher than if Trump wins" and use that as evidence about who to elect, and so on. But conditional predictions only predict the effect of an action if the event (e.g. who is elected) is unconfounded with the outcome (GDP). It may be that higher GDP and Biden being elected have a common cause, even if electing Biden does not increase GDP directly. One way to address this would be to have the market only pay out if Biden barely wins, or Trump barely wins, so that the confounders can be assumed to be in a similar state. Another strategy for identifying the causal effect would be to randomise. We can't randomise the election result, but we can randomise other quantities. For instance, "we generate a number from 1-100, and audit company X if we generate 1. If we generate the number 1, how much tax evasion will we find?". In general, in order to design action-guiding prediction markets, it may be important to draw on identification strategies from the causal inference literature.
I haven't yet checked for existing literature on this topic. Does anyone know of any?
Let's say that the election was decided by a coin flip, so there is no common cause between GDP and who is elected. Even in that case, I am still not sure why we'd think the prediction market would be useful for evaluating a counterfactual, such as "will GDP be higher under Biden or Trump?" The whole premise of prediction markets is that we evaluate them based on facts, not on counterfactuals. We might be able to use a forecaster's ability to predict facts in order to decide how much to trust them when they make counterfactual claims. But I don't see how we can evaluate a counterfactual claim via the prediction market mechanism directly. Can you elaborate on that?