Machine learning models such as graph neural networks, flexible causal-effect estimators, and causal discovery algorithms can capture complex structure in biomedical, materials, and neural data. Scientific and clinical use requires simpler or more structured representations. This dissertation asks what is preserved, and what is lost, when complex information is condensed into such forms. It argues that these representations should be judged by the decisions and conclusions they support.
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