Towards a Learning Theory of Causation

  • David Lopez-Paz | Cambridge University

We pose causal inference as the problem of learning to classify probability distributions. In particular, we assume access to a collection {(Si,li)}i=1n, where each Si is a sample drawn from the probability distribution of Xi × Yi, and li is a binary label indicating whether “Xi to Yi” or “Xi ≤ftarrow Yi”. Given these data, we build a causal inference rule in two steps. First, we featurize each Si using the kernel mean embedding associated with some characteristic kernel. Second, we train a binary classifier on such embeddings to distinguish between causal directions. We present generalization bounds showing the statistical consistency and learning rates of the proposed approach, and provide a simple implementation that achieves state-of-the-art cause-effect inference. Furthermore, we extend our ideas to infer causal relationships between more than two variables.

    • Portrait of Jeff Running

      Jeff Running

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