Identification for Prediction and Decision
Charles F. Manski's Identification for Prediction and Decision delves into the core principles of identification within statistical and econometric frameworks. It explores how to interpret data for accurate predictions and informed decisions, emphasizing the limits and possibilities of empirical analysis in uncertain environments. Ideal for economists and statisticians.
About This Book
Identification for Prediction and Decision by Charles F. Manski addresses key concepts in econometrics, focusing on how to draw reliable inferences from data.
The book examines the challenges of identifying parameters in models used for forecasting outcomes and informing choices under uncertainty.
Through rigorous analysis, it guides readers in understanding the boundaries of what can be learned from observational data.
This text serves as an essential resource for researchers and practitioners in social sciences seeking robust methodological tools.
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