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Matching on What Matters: A Pseudo-Metric Learning Approach to Matching Estimation in High Dimensions
When pre-processing observational data via matching, we seek to approximate each unit with maximally similar peers that had an alternative treatment status--essentially replicating a randomized block design.
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