Causality probability and time This book presents a new approach to causal inference and explanation addressing both the timing and complexity of relationships. The methods feasibility and success is demonstrated through theoretical and experimental case studies Whether we want to know the cause of a stocks price movements (in order to trade on this information) the key phrases that can alter public opinion of a candidate (in order to optimize a politicians speeches) or which genes work together to regulate a disease causing process (in order to intervene and disrupt it) many goals center on finding and using causes. Causes tell us not only that two phenomena are related but how they are related. They allow us to make robust predictions about the future explain the relationship between and occurrence of events and develop effective policies for intervention. While predictions are often made successfully on the basis of associations alone these relationships can be unstable. If we do not know why the resulting models work we cannot predict when they will stop working. Lung cancer rates in an area may be correlated with match sales if many smokers use matches to light their cigarettes but match sales may also be influenced by blackouts and seasonal trends (with many purchases around holidays or in winter). A spike in match sales due to a blackout will not result in the predicted spike in lung cancer rates but without knowledge of the underlying causes we would not be able to anticipate that failure. Models based on associations can also lead to redundancies since multiple effects of the true cause may be included as they are correlated with its occurrence. In applications to the biomedical domain this can result in unnecessary diagnostic tests that may be invasive and expensive