On Inductive Biases for Heterogeneous Treatment Effect Estimation
- URL: http://arxiv.org/abs/2106.03765v1
- Date: Mon, 7 Jun 2021 16:30:46 GMT
- Title: On Inductive Biases for Heterogeneous Treatment Effect Estimation
- Authors: Alicia Curth and Mihaela van der Schaar
- Abstract summary: We investigate how to exploit structural similarities of an individual's potential outcomes (POs) under different treatments.
We compare three end-to-end learning strategies to overcome this problem.
- Score: 91.3755431537592
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We investigate how to exploit structural similarities of an individual's
potential outcomes (POs) under different treatments to obtain better estimates
of conditional average treatment effects in finite samples. Especially when it
is unknown whether a treatment has an effect at all, it is natural to
hypothesize that the POs are similar - yet, some existing strategies for
treatment effect estimation employ regularization schemes that implicitly
encourage heterogeneity even when it does not exist and fail to fully make use
of shared structure. In this paper, we investigate and compare three end-to-end
learning strategies to overcome this problem - based on regularization,
reparametrization and a flexible multi-task architecture - each encoding
inductive bias favoring shared behavior across POs. To build understanding of
their relative strengths, we implement all strategies using neural networks and
conduct a wide range of semi-synthetic experiments. We observe that all three
approaches can lead to substantial improvements upon numerous baselines and
gain insight into performance differences across various experimental settings.
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