Assessing causal response to treatment intensity
’s machine learning
REGRESSION
We also move beyond the ITT effect to analyze how the number of ads a county receives affects the number of vaccines in the county, referred to as the ACR (The reason for this design is important: Unlike assignment to treatment or control status, the number of ads a county receives is not randomly assigned, but rather arises from the black box of Google’s machine learning predictions of viewers’ likelihood of engaging with the ad together with variation in competition for ad auctions. As such, it is possible that Google sends more ads to counties where viewers are more likely to be receptive, and hence a standard ordinary least squares (OLS) regression treating the number of ads as randomly assigned would not yield an unbiased estimate of the effect of ad exposure. (This does not bias the estimate of ITT effect, only the naïve estimate of the causal effect of an increase in ad exposure.) The IV approach, on the other hand, exploits the random assignment to restore a causal estimate of the response to an increase in the number of ads. Our setting also falls into the special case of one-sided noncompliance, meaning here that some treatment counties received no ads, but no control counties received ads (In our difference-in-difference regression framework, our IV regression is as follows