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What is the causal impact of R&D subsidies on firm growth?
in Data Science by Diamond (55,140 points) | 18 views

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Assume that we’re interested in the effect of R&D subsidies (let’s call this variable X) on firm growth (Y) . Standard ML techniques are very good at discerning the statistical relationship between these two variables, which we call P(Y|X). This last expression is the conditional probability of Y given X. Typically, you would find a pattern in the data according to which firms that receive an R&D subsidy will grow faster, compared to those that don’t. Estimating such conditional probabilities, in one form or the other, is nothing new. The thrust of modern ML techniques though—and the reason for the hype—comes from the fact that they are pretty **** good at the prediction task. Also, they deal well with non-standard data, such as pictures and speech, and big data.

But the seeing part is actually not what you’re interested here. Rather, policy makers care about what happens to the economy if they give out R&D grants to a typical firm. Will that action spur firm growth? In causal terminology, we’re interested in P(Y|do(X)), the probability of Y if we set X to a specific value. That’s the doing part, and there is a subtle but important difference between the two. Those firms that we observe to secure a grant can be very different from the rest of the population. Might be that they’re better connected, more innovative, or simply have a great idea for a project that is worth funding. So if we would start out paying R&D subsidies to average firms, we might not see an effect at all, because they might lack all these great characteristics.


by Diamond (55,140 points)

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