Interrupted Time Series Analysis is authored by David McDowall and published by Oxford University Press in an illustrated edition. The book delves into a comprehensive set of models and methods aimed at drawing causal inferences from time series data, focusing on AutoRegressive Integrated Moving Average (ARIMA) impact models. It provides detailed example analyses of social, behavioral, and biomedical time series to illustrate the application of these models. Additionally, it supplements the classic Box-Jenkins-Tiao model-building strategy with recent auxiliary tests for transformation, differencing, and model selection. The text also explores new developments in Bayesian hypothesis testing and synthetic control group designs, enhancing its relevance to contemporary research practices. Graphical illustrations are extensively used throughout the book to clarify complex concepts. With forty completed example analyses that highlight the implications of model properties, this inter-disciplinary work is designed for researchers with a background in time series data or cross-sectional regression analysis but limited experience in the structure of time series processes and experiments. The publication date is October 14, 2019, and it contains 200 pages.