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arXiv · 2209.00137

Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process

Aditya Kelvianto Sidharta
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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.

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Aditya Kelvianto Sidharta. 2022-08-31. Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process. https://arxiv.org/abs/2209.00137

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