DeepAI AI Chat
Log In Sign Up

MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming

10/17/2019
by   Yura Perov, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

10/20/2019

Amortized Rejection Sampling in Universal Probabilistic Programming

Existing approaches to amortized inference in probabilistic programs wit...
07/20/2022

Can Causal (and Counterfactual) Reasoning improve Privacy Threat Modelling?

Causal questions often permeate in our day-to-day activities. With causa...
05/27/2022

Counterfactual Analysis in Dynamic Models: Copulas and Bounds

We provide an explicit model of the causal mechanism in a structural cau...
05/31/2018

Approximate Knowledge Compilation by Online Collapsed Importance Sampling

We introduce collapsed compilation, a novel approximate inference algori...
03/13/2013

A computational scheme for Reasoning in Dynamic Probabilistic Networks

A computational scheme for reasoning about dynamic systems using (causal...
03/27/2013

Probabilistic Causal Reasoning

Predicting the future is an important component of decision making. In m...
08/12/2022

Probabilistic Variational Causal Effect as A new Theory for Causal Reasoning

In this paper, we introduce a new causal framework capable of dealing wi...