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In recent years, there has been an increasing interest in studying causality-related properties in machine learning models generally, and in generative models in particular.
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Learning the structure of probabilistic sentential decision diagrams
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A semantic loss function for deep learning with symbolic knowledge
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A. Choi, G. V. den Broeck, and A. Darwiche · 2015
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H. Zhao, M. Melibari, and P. Poupart · 2015
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On the latent variable interpretation in sum-product networks
R. Peharz, R. Gens, F. Pernkopf, and P. M. Domingos · 2016
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A. Choi and A. Darwiche · 2017
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M. Besserve, R. Sun, and B. Schölkopf · 2018
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Budgeted experiment design for causal structure learning
A. Ghassami, S. Salehkaleybar, N. Kiyavash, and E. Bareinboim · 2018
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Y. Liang and G. V. den Broeck · 2018
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Mixed sum-product networks: A deep architecture for hybrid domains
A. Molina, A. Vergari, N. Di Mauro, A. , F. Esposito, and K. Kersting · 2018
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The seven tools of causal inference, with reflections on machine learning
J. Pearl · 2019
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