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A growing family of approaches to causal inference rely on Bayesian formulations of assumptions that go beyond causal graph structure.
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Bayesian learning for neural networks , volume 118
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Multiverse: causal reasoning using importance sampling in probabilistic programming
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A critical look at the consistency of causal estimation with deep latent variable models
Rissanen, S. and Marttinen, P · 2021
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The causal-neural connection: Expressiveness, learnability, and inference
Xia, K., Lee, K.-Z., Bengio, Y., and Bareinboim, E · 2021
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Partial counterfactual identification from observational and experimental data
Zhang, J., Tian, J., and Bareinboim, E · 2021
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