MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
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 significantly 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.
Cite
@article{arxiv.1910.08091,
title = {MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming},
author = {Yura Perov and Logan Graham and Kostis Gourgoulias and Jonathan G. Richens and Ciarán M. Lee and Adam Baker and Saurabh Johri},
journal= {arXiv preprint arXiv:1910.08091},
year = {2020}
}
Comments
Logan and Yura have made equal contributions to the paper. Accepted to the 2nd Symposium on Advances in Approximate Bayesian Inference (Vancouver, Canada, 2019)