Agent-based Models and Causal Inference

Agent-based Models and Causal Inference is a book that argues that agent-based models can be an important tool in warranting causal claims, despite the lack of credence given to this possibility by scholars of causal inference. It compares the inferential threats to ABMs with traditional methods of RCTs, regression, and instrumental variables and concludes by looking at four examples where an analysis based on ABMs complements and augments the evidence for specific causal claims provided by other methods. The book is a valuable resource for researchers interested in understanding how patterns in society can arise in society and how computational modelling can help unravel the complexity of the real social world.

Format: Hardback
Length: 176 pages
Publication date: 03 March 2022
Publisher: John Wiley and Sons Ltd

Scholars of causal inference have given little credence to the possibility that agent-based models (ABMs) could be an important tool in warranting causal claims. However, Manzo's book makes a convincing case that this is a mistake. The book starts by describing the impressive progress that ABMs have made as a credible methodology in the last several decades. It then goes on to compare the inferential threats to ABMs versus the traditional methods of randomized controlled trials (RCTs), regression, and instrumental variables, showing that they have a common vulnerability of being based on untestable assumptions.

The book concludes by looking at four examples where an analysis based on ABMs complements and augments the evidence for specific causal claims provided by other methods. Manzo has done a most convincing job of showing that ABMs can be an important resource in any researcher's tool kit.

Christopher Winship, Diker-Tishman Professor of Sociology, Harvard University, USA, states: "Agent-based Models and Causal Inference is a first-rate contribution to the debate on, and practice of, causal claims. With exemplary rigor, systematic precision, and pedagogic clarity, this book contrasts the assumptions about causality that undergird agent-based models, experimental methods, and statistically based observational methods, discusses the challenges these methods face as far as inferences go, and, in light of this discussion, elaborates the case for combining these methods respective strengths: a remarkable achievement."

Ivan Ermakoff, Professor of Sociology, University of Wisconsin-Madison, USA, states: "Agent-based models are a uniquely powerful tool for understanding how patterns in society may arise in often surprising and counter-intuitive ways. This book offers a strong and deeply reflected argument.

Weight: 460g
Dimension: 175 x 250 x 19 (mm)
ISBN-13: 9781119704478