Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies
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Author / Producer
Date
2021
Publication Type
Conference Paper
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yes
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Abstract
Estimating individualized treatment effects (ITEs) from observational data is crucial for decision-making. In order to obtain unbiased ITE estimates, a common assumption is that all confounders are observed. However, in practice, it is unlikely that we observe these confounders directly. Instead, we often observe noisy measurements of true confounders, which can serve as valid proxies. In this paper, we address the problem of estimating ITE in the longitudinal setting where we observe noisy proxies instead of true confounders. To this end, we develop the Deconfounding Temporal Autoencoder (DTA), a novel method that leverages observed noisy proxies to learn a hidden embedding that reflects the true hidden confounders. In particular, the DTA combines a long short-term memory autoencoder with a causal regularization penalty that renders the potential outcomes and treatment assignment conditionally independent given the learned hidden embedding. Once the hidden embedding is learned via DTA, state-of-the-art outcome models can be used to control for it and obtain unbiased estimates of ITE. Using synthetic and real-world medical data, we demonstrate the effectiveness of our DTA by improving over state-of-the-art benchmarks by a substantial margin.
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Publication status
published
Book title
Proceedings of Machine Learning for Health
Journal / series
Volume
158
Pages / Article No.
143 - 155
Publisher
PMLR
Event
Machine Learning for Health (ML4H)
Edition / version
Methods
Software
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Date collected
Date created
Subject
individualized treatment effects; time-varying hidden confounders; noisy proxies; causal machine learning
Organisational unit
09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
Notes
Funding
186932 - Data-driven health management (SNF)