MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
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.
Logan and Yura have made equal contributions to the paper. Accepted to the 2nd Symposium on Advances in Approximate Bayesian Inference (Vancouver, Canada, 2019)
References in corpus (15)
- Counterfactual Fairness
- Counterfactual Multi-Agent Policy Gradients
- MADE: Masked Autoencoder for Distribution Estimation
- Local Rule-Based Explanations of Black Box Decision Systems
- Reliable Decision Support using Counterfactual Models
- Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
- Simple, Distributed, and Accelerated Probabilistic Programming
- Deep Amortized Inference for Probabilistic Programs
- A Compilation Target for Probabilistic Programming Languages
- Design and Implementation of Probabilistic Programming Language Anglican
- Recognition Networks for Approximate Inference in BN20 Networks
- Data-driven Sequential Monte Carlo in Probabilistic Programming
- Counterfactual diagnosis
- Applications of Probabilistic Programming (Master's thesis, 2015)
- Universal Marginalizer for Amortised Inference and Embedding of Generative Models