Agent-Based Models and Causal Inference Contributor(s): Manzo, Gianluca (Author) |
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ISBN: 1119704472 ISBN-13: 9781119704478 Publisher: Wiley OUR PRICE: $87.35 Product Type: Hardcover - Other Formats Published: February 2022 |
Additional Information |
BISAC Categories: - Mathematics | Probability & Statistics - General - Social Science | Research |
Dewey: 300.721 |
LCCN: 2021032403 |
Physical Information: 0.44" H x 6.69" W x 9.61" (1.06 lbs) 176 pages |
Descriptions, Reviews, Etc. |
Publisher Description: Explore the issue of causal inference in agent-based computational models in a first-of-it's-kind volume Agent-based Models and Causal Inference delivers an insightful investigation into the conditions under which different quantitative methods can legitimately hold to be able to establish causal claims. The book compares agent-based computational methods with randomized experiments, instrumental variables, and various types of causal graphs. It goes on to explain why there is no strong argument to believe that observational and experimental methods are qualitatively superior to simulation-based methods in their capacity to contribute to establishing causal claims. Organized in two parts, Agent-based Models and Causal Inference connects the literature from various fields, including causality, social mechanisms, statistical and experimental methods for causal inference, and agent-based computation models to help show that causality means different things within different methods for causal analysis, and that persuasive causal claims can only be built at the intersection of these various methods.
Perfect for academic social scientists and scholars in the fields of computational social science, philosophy, statistics, experimental design, and ecology, Agent-based Models and Causal Inference will also earn a place in the libraries of PhD students seeking a one-stop reference on the issue of causal inference in agent-based computational models. |