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Abstract:
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I will present a matching framework for causal inference in the potential outcomes setting called Almost Matching Exactly. This framework has several important elements: (1) It creates matched groups that are interpretable. The goal is to match treatment and control units on as many covariates as possible. (2) It creates accurate estimates of individual treatment effects. This is because we use machine learning on a separate training set to learn which features are important for matching. The key constraint is that units are always matched on a set of covariates that together can predict the outcome well. (3) It is fast and scalable. It scales to datasets that are so large that they do not fit in main memory, owing to techniques from the field of databases. Its run-time on a single processor is also fast, owing to efficient bit-vector computations. In summary, these methods rival black box machine learning methods in their estimation accuracy but have the benefit of being interpretable and easier to troubleshoot. Our lab website is here: https://almostmatchingexactly.github.io
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