MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
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Proceedings of The 2nd Symposium on
Advances in Approximate Bayesian Inference, PMLR 118:136, 2020.
Abstract
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programming. By considering the structure of the counterfactual query, one can signicantly optimise the inference process. We also consider design choices to enable further optimisations. We introduce MultiVerse, a probabilistic programming prototype engine for approximate causal reasoning. We provide experimental results and compare with Pyro, an existing probabilistic programming framework with some of causal reasoning tools.
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