This workshop explores the use of causality with predictive models in order to assess the results of given actions. Such assessment is essential in many domains, including epidemiology, medicine, ecology, economy, sociology and business. Predictive models simply based on event correlations do not model mechanisms. They allow us to make predictions in a stationary environment (no change in the distribution of all the variables), but do not allow us to predict the consequence of given actions. For instance, smoking and coughing are both predictive of respiratory disease. One is a cause and the other a symptom. Acting on the cause can change the disease state, but not acting on the symptom. Understanding the effect of interventions has been the goal of most causal models but their complexity has limited their use to a few hundreds variables. Feature selection on the other hand can handle thousands of variables at the same time but does not make a difference between causes and symptoms. By confronting the hypothesis underlying causality and feature selection approaches, this workshop aims at investigating new approaches to extract causal relationships from data.