Causal inference with Machine Learning-Based Covariate Representation
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Abstract (arXiv)
Utilizing covariate information has been a powerful approach to improve the efficiency and accuracy for causal inference, which support massive amount of randomized experiments run on data-driven enterprises. However, state-of-art approaches can become practically unreliable when the dimension of covariate increases to just 50, whereas experiments on large platforms can observe even higher dimension of covariate. We propose a machine-learning-assisted covariate representation approach that can effectively make use of historical experiment or observational data that are run on the same platform to understand which lower dimensions can effectively represent the higher-dimensional covariate. We then propose design and estimation methods with the covariate representation. We prove statistically reliability and performance guarantees for the proposed methods. The empirical performance is demonstrated using numerical experiments.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 3 Nov 2023.
Builds on
16 of 28 references.
References · 16
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4 more
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Citing papers per year: 2026: 1.
1 paper.