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Recently, it has been shown how sampling actions from the predictive distribution over the optimal action-sometimes called Thompson sampling-can be applied to solve sequential adaptive control problem...
Many algorithms for inferring causality rely heavily on the faithfulness assumption.The main justi cation for imposing this assumption is that the set of unfaithful distribu-tions has Lebesgue measure...
Causal inference uses observations to infer the causal structure of the data generating system.We study a class of functional models that we call Time Series Models with Independent Noise (TiMINo). Th...
The effect of vigorous physical activity on mortality in the elderly is difficult to estimate using conventional approaches to causal inference that define this effect by comparing the mortality risks...
A new class of Marginal Structural Models (MSMs), History-Restricted MSMs (HRMSMs), was recently introduced for longitudinal data for the purpose of defining causal parameters which may often be bette...
A new class of Marginal Structural Models (MSMs), History- Restricted MSMs (HRMSMs), was recently introduced for longitudinal data for the purpose of defining causal parameters which may often be be...

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