Causal Inference using Bayesian non-parametric quasi-experimental designs for sequential data
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2020-07-01
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en
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Abstract
In many domains, such as healthcare, it is crucial to know as fast as possible whether
an intervention (e.g. drug treatment) had an e ect. In these contexts, time is of the
essence and samples are both costly to acquire and limited in their amount, as they
only arrive sequentially over time. This research proposes a method called Sequential
Bayesian non-parametric quasi-experimental designs (S-BNQD) which allows to establish
a causal e ect in sequential settings. S-BNQD is based on the Bayesian non-parametric
quasi-experimental design (BNQD) (Hinne, van Gerven, & Ambrogioni, 2019) which is
a Bayesian version of quasi-experimental designs. BNQD is based on Gaussian Process
Regression which makes it particularly attractive for sequential designs due to its
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bility and uncertainty estimation (M. M. Zhang, Dumitrascu, Williamson, Engelhardt,
& Oct, 2019). The new framework can be applied in various contexts where Randomized
Control Trials (RCT's) are not practical and it is crucial to know as reliably and quickly
as possible whether an intervention was e ective or not.
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Faculteit der Sociale Wetenschappen