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Computational approaches for drug repurposing can accelerate the identification of treatments during a pandemic. In this Review, the authors discuss this topic in the context of COVID-19 and propose a strategy to make computational drug repurposing more effective in future pandemics.
While estimating causality from observational data is challenging, quasi-experiments provide causal inference methods with plausible assumptions that can be practical to a range of real-world problems.
