MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
This work addresses causal reasoning challenges for researchers and practitioners in probabilistic programming, but it appears incremental as it builds on existing importance sampling methods with optimizations.
The paper tackles the problem of causal reasoning, specifically counterfactual inference, by using importance sampling implemented natively in probabilistic programming, resulting in a prototype engine called MultiVerse that shows experimental improvements compared to existing frameworks like Pyro.
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.