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From Dependence to Causation
Machine learning is the science of discovering statistical dependencies in
data, and the use of those dependencies to perform predictions. During the last
decade, machine learning has made spectacular progress, surpassing human
performance in complex tasks such as object recognition, car driving, and
computer gaming. However, the central role of prediction in machine learning
avoids progress towards general-purpose artificial intelligence. As one way
forward, we argue that causal inference is a fundamental component of human
intelligence, yet ignored by learning algorithms.
Causal inference is the problem of uncovering the cause-effect relationships
between the variables of a data generating system. Causal structures provide
understanding about how these systems behave under changing, unseen
environments. In turn, knowledge about these causal dynamics allows to answer
"what if" questions, describing the potential responses of the system under
hypothetical manipulations and interventions. Thus, understanding cause and
effect is one step from machine learning towards machine reasoning and machine
intelligence. But, currently available causal inference algorithms operate in
specific regimes, and rely on assumptions that are difficult to verify in
practice.
This thesis advances the art of causal inference in three different ways.
First, we develop a framework for the study of statistical dependence based on
copulas and random features. Second, we build on this framework to interpret
the problem of causal inference as the task of distribution classification,
yielding a family of novel causal inference algorithms. Third, we discover
causal structures in convolutional neural network features using our
algorithms. The algorithms presented in this thesis are scalable, exhibit
strong theoretical guarantees, and achieve state-of-the-art performance in a
variety of real-world benchmarks.Comment: PhD thesi