Causality for Machine Learning

Humans care about the the causality underlying the world. But Why? Is it useful? (How?) How can the causal knowledge be useful for statistical learning and inference that are conventionally considered “non-causal”?

Few-shot domain adaptation by causal mechanism transfer (Paper)

This work approaches the central question of domain adaptation (DA): “when is it possible?” DA is an approach to enable small-data learning by using some additional data sets which are related to but different from your own data. The central question in DA is “what do we assume on the relations of the data sets?” (the question of transfer assumption). To provide one possible answer, we focus on the concept of causal mechanisms behind the data, and consider the scenario that the different data sets share a common causal mechanism. Technically, we consider so-called structural causal models, and assume that the set of structural equations is invariant across different domains. In this setting, we propose causal mechanism transfer, a method to exploit the assumption to perform DA. We conducted solid theoretical analyses of the method and performed a proof-of-concept experiment to confirm its validity.

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Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation (Paper)

In real-world tasks, there are situations where the domain experts can provide a rough estimate of the underlying causal graph of the data variables. A causal graph is known to encode the conditional independence relations that should hold in the data, and this can be a strong prior knowledge for predictive tasks of machine learning when the data is scarce. However, how should we incorporate this knowledge into a predictive modeling? This work addresses the question by proposing a model-agnostic data augmentation method that can be combined with virtually any supervised learning methods.

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