This is a basic prototype implementation of probabilistic programming MultiVerse for performing counterfactual inference using importance sampling.
Note that currently only one inference request at a time per Python interpreter (process) is supported.
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming by Yura Perov (equal contribution), Logan Graham (equal contribution), Kostis Gourgoulias, Jonathan G. Richens, Ciarán M. Lee, Adam Baker, Saurabh Johri.
Accepted to the 2nd Symposium on Advances in Approximate Bayesian Inference (2019) and to the Second International Conference on Probabilistic Programming (2020).
URL: https://arxiv.org/abs/1910.08091
A suggested BibTeX:
@misc{babylon2019multiverse,
title={Multi{V}erse: {C}ausal reasoning using importance sampling in probabilistic programming},
author={Perov, Yura and Graham, Logan and Gourgoulias, Kostis and Richens, Jonathan G. and Lee, Ciar{\'a}n M. and Baker, Adam and Johri, Saurabh},
url={https://arxiv.org/abs/1910.08091},
year={2019},
note={Logan and Yura have contributed equally. Accepted to the 2nd Symposium on Advances in Approximate Bayesian Inference (2019) and to the Second International Conference on Probabilistic Programming (2020).}
}
python -m pytest tests/
Basic code formatting checks:
make lint
Copyright 2019-2020 Babylon Health (Babylon Partners Limited).
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