MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming

2019/10/17 by Yura Perov, Perov, Yura, Logan Graham +11 · 1 citation
Computer Science · #Bayesian Modeling and Causal Inference #AI-based Problem Solving and Planning #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.1910.08091

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.

Cited by

• Leveraging directed causal discovery to detect latent common causes

Related

• Probabilistic Reasoning across the Causal Hierarchy
• Probabilistic Modelling is Sufficient for Causal Inference
• Whittemore: An embedded domain specific language for causal programming
• Computational Causal Inference
• Bayesian causal inference via probabilistic program synthesis