Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process
Machine Learning
2022-09-02 v1
Abstract
This paper investigates the problem of bounding possible output from a counterfactual query given a set of observational data. While various works of literature have described methodologies to generate efficient algorithms that provide an optimal bound for the counterfactual query, all of them assume a finite-horizon causal diagram. This paper aims to extend the previous work by modifying Q-learning algorithm to provide informative bounds of a causal query given an infinite-horizon causal diagram. Through simulations, our algorithms are proven to perform better compared to existing algorithm.
Cite
@article{arxiv.2209.00137,
title = {Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process},
author = {Aditya Kelvianto Sidharta},
journal= {arXiv preprint arXiv:2209.00137},
year = {2022}
}