  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.
