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We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches. We propose a new algorithm that exploits the causal feedback and(More)
It is usual to consider data protection and learnability as conflicting objectives. This is not always the case: we show how to jointly control causal inference — seen as the attack — and learnability by a noise-free process that mixes training examples, the Crossover Process (CP). One key point is that the CP is typically able to alter joint distributions(More)
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