科图分类法:	TP301.5 版次：
中图分类法:	TP301.5 版次：
著者:	Kleinberg, Samantha,
题名:	Causality, probability, and time / / ,
出版发行:	出版地： Cambridge : 出版社： Cambridge University Press, 出版日期： 2013.
载体形态:	vii, 259 p. : ill. ; 25 cm
内容提要:	"This book presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships. The method's feasibility and success is demonstrated through theoretical and experimental case studies"--Provided by publisher.
内容提要:	"Whether we want to know the cause of a stock's 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 politician's 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 w