MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming

Yura Perov, Logan Graham, Kostis Gourgoulias, Jonathan Richens, Ciaran Lee, Adam Baker, Saurabh Johri
Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, PMLR 118:1-36, 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v118-perov20a, title = {MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming }, author = {Perov, Yura and Graham, Logan and Gourgoulias, Kostis and Richens, Jonathan and Lee, Ciaran and Baker, Adam and Johri, Saurabh}, booktitle = {Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference}, pages = {1--36}, year = {2020}, editor = {Zhang, Cheng and Ruiz, Francisco and Bui, Thang and Dieng, Adji Bousso and Liang, Dawen}, volume = {118}, series = {Proceedings of Machine Learning Research}, month = {08 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v118/perov20a/perov20a.pdf}, url = {https://proceedings.mlr.press/v118/perov20a.html}, 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.} }
Endnote
%0 Conference Paper %T MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming %A Yura Perov %A Logan Graham %A Kostis Gourgoulias %A Jonathan Richens %A Ciaran Lee %A Adam Baker %A Saurabh Johri %B Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference %C Proceedings of Machine Learning Research %D 2020 %E Cheng Zhang %E Francisco Ruiz %E Thang Bui %E Adji Bousso Dieng %E Dawen Liang %F pmlr-v118-perov20a %I PMLR %P 1--36 %U https://proceedings.mlr.press/v118/perov20a.html %V 118 %X 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.
APA
Perov, Y., Graham, L., Gourgoulias, K., Richens, J., Lee, C., Baker, A. & Johri, S.. (2020). MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming . Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, in Proceedings of Machine Learning Research 118:1-36 Available from https://proceedings.mlr.press/v118/perov20a.html.

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