Sequential Deconfounding for Causal Inference with Unobserved Confounders.

Published on Apr 16, 2021in arXiv: Methodology
Tobias Hatt2
Estimated H-index: 2
,
Stefan Feuerriegel23
Estimated H-index: 23
Sources
Abstract
Using observational data to estimate the effect of a treatment is a powerful tool for decision-making when randomized experiments are infeasible or costly. However, observational data often yields biased estimates of treatment effects, since treatment assignment can be confounded by unobserved variables. A remedy is offered by deconfounding methods that adjust for such unobserved confounders. In this paper, we develop the Sequential Deconfounder, a method that enables estimating individualized treatment effects over time in presence of unobserved confounders. This is the first deconfounding method that can be used in a general sequential setting (i.e., with one or more treatments assigned at each timestep). The Sequential Deconfounder uses a novel Gaussian process latent variable model to infer substitutes for the unobserved confounders, which are then used in conjunction with an outcome model to estimate treatment effects over time. We prove that using our method yields unbiased estimates of individualized treatment responses over time. Using simulated and real medical data, we demonstrate the efficacy of our method in deconfounding the estimation of treatment responses over time.
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2020ICML: International Conference on Machine Learning
References46
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#1Yilmazcan 脰zyurt (ETH Zurich)H-Index: 1
#2Mathias Kraus (FAU: University of Erlangen-Nuremberg)H-Index: 7
Last. Stefan Feuerriegel (ETH Zurich)H-Index: 23
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Clinical practice in intensive care units (ICUs) requires early warnings when a patient's condition is about to deteriorate so that preventive measures can be undertaken. To this end, prediction algorithms have been developed that estimate the risk of mortality in ICUs. In this work, we propose a novel generative deep probabilistic model for real-time risk scoring in ICUs. Specifically, we develop an attentive deep Markov model called AttDMM. To the best of our knowledge, AttDMM is the first ICU...
Jul 12, 2020 in ICML (International Conference on Machine Learning)
#1Sam Witty (UMass: University of Massachusetts Amherst)H-Index: 3
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Last. Vikash K. Mansinghka (MIT: Massachusetts Institute of Technology)H-Index: 21
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Latent confounders---unobserved variables that influence both treatment and outcome---can bias estimates of causal effects. In some cases, these confounders are shared across observations, e.g. all students taking a course are influenced by the course's difficulty in addition to any educational interventions they receive individually. This paper shows how to semiparametrically model latent confounders that have this structure and thereby improve estimates of causal effects. The key innovations a...
Wang and Blei (2019) studies multiple causal inference and proposes the deconfounder algorithm. The paper discusses theoretical requirements and presents empirical studies. Several refinements have been suggested around the theory of the deconfounder. Among these, Imai and Jiang clarified the assumption of "no unobserved single-cause confounders." Using their assumption, this paper clarifies the theory. Furthermore, Ogburn et al. (2020) proposes counterexamples to the theory. But the proposed co...
#1Yixin Wang (Columbia University)H-Index: 10
#2David M. Blei (Columbia University)H-Index: 97
AbstractCausal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods assume that we observe all confounders, variables that affect both the causal...
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#1Linying ZhangH-Index: 4
#2Yixin WangH-Index: 10
Last. George HripcsakH-Index: 79
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The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. However, assessing a treatment effect from EHRs is challenging: it is biased by unobserved confounders, unmeasured variables that affect both patients' medical prescription and the...
#1Kosuke Imai (Harvard University)H-Index: 52
#2Zhichao JiangH-Index: 6
This commentary has two goals. We first critically review the deconfounder method and point out its advantages and limitations. We then briefly consider three possible ways to address some of the limitations of the deconfounder method.
#1Yixin Wang (Columbia University)H-Index: 10
#2David M. Blei (Columbia University)H-Index: 97
We thank all the discussants for taking the time and energy to build on this work; and we thank the editors for putting together an engaging and thought-provoking collection of discussions. After r...
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#1Susan Athey (Stanford University)H-Index: 67
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We congratulate the authors of Wang and Blei (2018) on a thought-provoking article on causal inference in settings with unobserved confounders. We expect that their ideas will lead to further devel...
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#1Elizabeth L. Ogburn (Johns Hopkins University)H-Index: 25
#2Ilya Shpitser (Johns Hopkins University)H-Index: 23
Last. Eric J. Tchetgen Tchetgen (UPenn: University of Pennsylvania)H-Index: 51
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We are grateful to Wang and Blei (2019) (hereafter WB) for drawing attention to the important and increasingly popular project of using latent variable methods to control for unmeasured confounding...
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May 24, 2019 in ICML (International Conference on Machine Learning)
#1Kaspar M盲rtens (University of Oxford)H-Index: 5
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The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. However, in many real-world problems these representations may not be sufficient to aid interpretation on their own, and it would be desirable to interpret the model in terms of the original features themselves. Our goal is to characterise how feature-level variation depends on latent low-dimensional representations, externa...
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