Web: http://arxiv.org/abs/1811.07415

Jan. 28, 2022, 2:11 a.m. | Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

cs.LG updates on arXiv.org arxiv.org

We introduce a flexible framework that produces high-quality almost-exact
matches for causal inference. Most prior work in matching uses ad-hoc distance
metrics, often leading to poor quality matches, particularly when there are
irrelevant covariates. In this work, we learn an interpretable distance metric
for matching, which leads to substantially higher quality matches. The learned
distance metric stretches the covariate space according to each covariate's
contribution to outcome prediction: this stretching means that mismatches on
important covariates carry a larger penalty …

arxiv cross learning

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