I want to understand the effect of exposure to my brands advertisements on an individual's likelihood to purchase a product from my store, accounting for the effect that interest in athletics has on an individual's exposure to my advertisements and their purchasing decisions. In this blog post and the next, I will present a detailed set of steps for leveraging propensity score matching in order to effectively eliminate confounding bias from a large set of variables.įigure 1: A depiction of the causal relationship we are interested in estimating. Propensity score matching can be used in marketing to estimate a “lift” in revenue due to a particular digital advertising campaign, in medicine to identify the precise effect that smoking has on a variety of health outcomes, and in economics to measure the benefits of socioeconomic support programs in the US. Propensity score matching is a quite powerful, rather straightforward backdoor adjustment technique typically used in the presence of selection bias or bias arising from differences between those who are selected to receive treatment and those who are not. In my discussion of back-door adjustment strategies I briefly mentioned propensity score matching a useful technique for reducing a set of confounding variables to a single propensity score in order to effectively control for confounding bias 1. In my previous post, I presented a rigorous definition for confounding bias as well as a general taxonomy comprising of two sets of strategies, back-door and front-door adjustments, for eliminating it. How can we estimate average treatment effects and what biases must we be wary of when evaluating our estimation? When quantifying the causal effect of a proposed intervention, we wish to estimate the average causal effect this intervention will have on individuals in our dataset. Estimating Average Treatment Effects June 07, 2020
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