![]() Specifically, I provided a simple procedure for estimating propensity scores, matching individuals based on this estimation, and calculating a measurement of a causal effect by comparing observed individuals within the same match. In that post, I described a scenario in which a marketer may struggle to identify the causal effect of a particular campaign, and discussed a rigorous causal inference technique built to solve this problem. In this post I present the obstacles we may face when leveraging these models as well as the 'adjustments' we can make to remove them.Īs discussed in my previous blog post, propensity score matching is a powerful technique for reducing a set of confounding variables to a single propensity score, so an analyst can easily eliminate all confounding bias. So far, our discussions of causality have been rather straightforward: we've defined models for describing the world and analyzed their implications.
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