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We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky.
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Kleinegesse, S. and Gutmann, M · 2020
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Foster, A., Ivanova, D. R., Malik, I., and Rainforth, T · 2021
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Ghassami, A., Salehkaleybar, S., Kiyavash, N., and Bareinboim, E · 2018
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