Countless books and articles on data science and analytics discuss descriptive analytics, predictive analytics, and prescriptive analytics. An additional analytics area that is much less discussed links this world of analytics, with its statistical model-based descriptions and predictions, to the world of practical decisions in which actions have consequences that decision-makers, and perhaps other stake-holders, care about, and about which they are often uncertain. This is the area of causal analytics. How causal analytics relates to other analytics areas and how its methods can be used to predict what to expect next, explain past outcomes and observations, prescribe what to do next to improve future outcomes, and evaluate how well past or current policies accomplish their intended goals—for whom, and under what conditions—are the main topics of this book.
CITATION STYLE
Cox, L. A., Popken, D. A., & Sun, R. X. (2018). Causal analytics and risk analytics. In International Series in Operations Research and Management Science (Vol. 270, pp. 3–95). Springer New York LLC. https://doi.org/10.1007/978-3-319-78242-3_1
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