More interesting and less widely known—even in Silicon Valley—is that successful predictions of the effects of interventions can sometimes be made even without an experiment. For example, the sales manager could develop a model of consumer behavior that includes market conditions. Even if she doesn’t have data on every factor, she might have data on enough key surrogates to make the prediction. A sufficiently strong and accurate causal model can allow us to use rung-one (observational) data to answer rung-two (interventional) queries. Without the causal model, we could not go from rung one to
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