Learning how to act: making good decisions with machine learning
Date
2017
Authors
Lattimore, Finnian Rachel
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Abstract
This thesis is about machine learning and statistical approaches
to decision making. How can we learn from data to anticipate the
consequence of, and optimally select, interventions or actions?
Problems such as deciding which medication to prescribe to
patients, who should be released on bail, and how much to charge
for insurance are ubiquitous, and have far reaching impacts on
our lives. There are two fundamental approaches to learning how
to act: reinforcement learning, in which an agent directly
intervenes in a system and learns from the outcome, and
observational causal inference, whereby we seek to infer the
outcome of an intervention from observing the system.
The goal of this thesis to connect and unify these key
approaches. I introduce causal bandit problems: a synthesis that
combines causal graphical models, which were developed for
observational causal inference, with multi-armed bandit problems,
which are a subset of reinforcement learning problems that are
simple enough to admit formal analysis. I show that knowledge of
the causal structure allows us to transfer information learned
about the outcome of one action to predict the outcome of an
alternate action, yielding a novel form of structure between
bandit arms that cannot be exploited by existing algorithms. I
propose an algorithm for causal bandit problems and prove bounds
on the simple regret demonstrating it is close to mini-max
optimal and better than algorithms that do not use the additional
causal information.
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Keywords
machine learning, causal inference, causality, reinforcement learning, multi-armed bandits
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Thesis (PhD)
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DOI
10.25911/5d67b766194ec