Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders

ICML 2020 Ioana BicaAhmed M. AlaaMihaela van der Schaar

The estimation of treatment effects is a pervasive problem in medicine. Existing methods for estimating treatment effects from longitudinal observational data assume that there are no hidden confounders, an assumption that is not testable in practice and, if it does not hold, leads to biased estimates... (read more)

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