We present a place-history-based activity prediction system called Agatha, in order to enable activity-aware mobile services in smart cities. The system predicts a user's potential subsequent activities that are highly likely to occur given a series of information about activities done before or activity-related contextual information such as visit place and time. To predict the activities, we develop a causality-based activity prediction model using Bayesian networks. The basic idea of the prediction is that where a person has been and what he/she has done so far influence what he/she will do next. To show the feasibility, we evaluate the prediction model using the American Time-Use Survey (ATUS) dataset, which includes more than 10,000 people's location and activity history. Our evaluation shows that Agatha can predict users' potential activities with up to 90% accuracy for the top 3 activities, more than 80% for the top 2 activities, and about 65% for the top 1 activity while considering a relatively large number of daily activities defined in the ATUS dataset, that is, 17 activities.