Agent-based Models and Causal Inference by Gianluca Manzo
Requirements: .ePUB, .PDF reader, 3 MB
Overview: Agent-based Models and Causal Inference
Scholars of causal inference have given little credence to the possibility that ABMs could be an important tool in warranting causal claims. 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 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
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.
Genre: Non-Fiction > Educational
Download Instructions:
https://ouo.io/IehXvB
https://ouo.io/q2INCY
