How to Use Propensity Score Matching to Measure Down Stream Causal Impact of an Event

This story was originally published on HackerNoon at: https://hackernoon.com/how-to-use-propensity-score-matching-to-measure-down-stream-causal-impact-of-an-event. How can we know ours ads are making impact that we aim for? What if targeted ads are not working the way we want them to? Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #data-analytics, #statistics, #analytics, #advertising, #big-data-analytics, #hackernoon-top-story-tag, #propensity-score-matching, and more. This story was written by: @dharmateja. Learn more about this writer by checking @dharmateja's about page, and for more stories, please visit hackernoon.com. Ad exposure is not randomly assigned – algorithms may show ads more to highly active users. As a result, “unobservable factors make exposure endogenous,” meaning there are hidden biases in who sees the ad. This is where propensity score matching (PSM) comes in – it’s a statistical way to create apples-to-apples comparisons.