This text provides a system that can be used in the analysis of the predictions that social and behavioural scientists can make from non- experimental and experimental data. It draws on examples from criminology, demography, epidemiology, social psychology and sociology as well as economics.
Focusing on the intersection of economics and decision-making, this book explores how treatment responses can inform choices for diverse populations. It addresses the challenges faced by social planners, such as physicians and judges, in applying research findings to real-world scenarios. The text emphasizes the importance of utilizing existing evidence effectively, despite the limitations of current research, to optimize treatment decisions for heterogeneous groups.
Focusing on the intersection of welfare economics, decision theory, and econometrics, this book explores how these fields can collaboratively address uncertainty in public policy formation. By highlighting the lack of interaction among them, it encourages researchers to engage with one another's perspectives and methodologies, fostering constructive dialogue and enhancing the understanding of shared challenges in planning under uncertainty.
For the past few years, the author, a renowned economist, has been applying the statistical tools of economics to decision making under uncertainty in the context of patient health status and response to treatment. He shows how statistical imprecision and identification problems affect empirical research in the patient-care sphere.
The book presents in a rigorous and thorough manner the main elements of Charles Manski's research on partial identification of probability distributions. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric. There is an enormous scope for fruitful inference using data and assumptions that partially identify population parameters.