Learning Overlapping Representations for the Estimation of Individualized Treatment Effects

• URL: http://arxiv.org/abs/2001.04754v3
• Date: Mon, 17 Feb 2020 12:07:29 GMT
• Title: Learning Overlapping Representations for the Estimation of Individualized Treatment Effects
• Authors: Yao Zhang, Alexis Bellot, Mihaela van der Schaar
• Abstract summary: Estimating the likely outcome of alternatives from observational data is a challenging problem. We show that algorithms that learn domain-invariant representations of inputs are often inappropriate. We develop a deep kernel regression algorithm and posterior regularization framework that substantially outperforms the state-of-the-art on a variety of benchmarks data sets.
• Score: 97.42686600929211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The choice of making an intervention depends on its potential benefit or harm in comparison to alternatives. Estimating the likely outcome of alternatives from observational data is a challenging problem as all outcomes are never observed, and selection bias precludes the direct comparison of differently intervened groups. Despite their empirical success, we show that algorithms that learn domain-invariant representations of inputs (on which to make predictions) are often inappropriate, and develop generalization bounds that demonstrate the dependence on domain overlap and highlight the need for invertible latent maps. Based on these results, we develop a deep kernel regression algorithm and posterior regularization framework that substantially outperforms the state-of-the-art on a variety of benchmarks data sets.

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