Does smoking cause cancer? Can we find the causal direction between two variables by analyzing their observed values? In our daily life and science, people often attempt to answer such causal questions, for the purpose of understanding and manipulating systems properly. In the past decades, interesting advances were made in fields including machine learning, statistics, and philosophy in order to answer such questions. Furthermore, we are also often concerned with how to do machine learning in complex environments. For instance, how can we make optimal predictions in non-stationary environments? Interestingly, it has recently been shown that causal information can facilitate understanding and solving various machine learning problems, including transfer learning and semi-supervised learning. This talk reviews essential concepts in causality studies and is focused on how to learn causal relations from observation data and why and how the causal perspective helps in machine learning and other tasks. Finally, I will discuss why causal representations matter in order to achieve general-purpose artificial intelligence.