Metadata only
Datum
2024Typ
- Conference Paper
ETH Bibliographie
yes
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Abstract
Observational data is often used to estimate the effect of a treatment 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 with a single treatment 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. Mehr anzeigen
Publikationsstatus
publishedExterne Links
Buchtitel
Proceedings of the Third Conference on Causal Learning and ReasoningZeitschrift / Serie
Proceedings of Machine Learning ResearchBand
Seiten / Artikelnummer
Verlag
PMLRKonferenz
Organisationseinheit
09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
Förderung
186932 - Data-driven health management (SNF)
Anmerkungen
Poster presentation on April 2, 2024.ETH Bibliographie
yes
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