Identification Problems in the Social Sciences

Rating:
Summary: Little great book
Review: (From the back cover) "This book provides a language and a set of tools for finding bounds on the predictions that social and behavioural scientists can logically make from nonexperimental and experimental data. (...) draws on examples from criminology, demography, epidemiology, social psychology, and sociology as well economics to illustrate this language and to demonstrate the broad usefulness of the tools".

This is a little brilliant book on the relation between data, the use of assumptions, and identification of parameters or distributions that social scientists may be interested in. In the words of the author, "this book examines the conditional predictions that can and cannot be made given specified assumptions and empirical evidence". The beauty of this book is indeed its emphasis on the link between maintained assumptions and features of a population that can be identified. Manski has spent a large part of his career emphasizing that results derived from strong arbitrary assumptions do not have much scientific value. In some cases, calculating BOUNDS for parameters of interest can be much better than having a point estimate obtained by "denying" lack of knowledge of important aspects of reality. One lesson you will derive from this book is "we need to develop a greater tolerance for ambiguity".

Here is an example of the many empirical problems dealt with in the book (which is mostly methodological, and hence technical enough to be not suited for a lay reader). Suppose you have a sample of homeless individuals and you want to study the reasons why they may still be homeless some months afterwards. However, months later you only have information on a subset of your initial sample. Without making any assumptions about what causes individuals to exit your sample, what can you learn? Can you learn more if you make assumptions on the causes that lead individuals out of your sample?

Another reviewer has described this book as an introduction to "nonparametric estimation". This is susprising and totally misleading, as there is close to NOTHING in this book about estimation, parametric or otherwise. This book is about identification. That is, the question addressed here is always something like the following: suppose that you have perfect knowledge of certain features of the data, and suppose that you are interested in certain other features of the data. What kind of assumptions do you need to make in order to be able to learn about these other features? How does your ability to learn changes when you change the assumptions and/or the initial information? This book will NOT tell you how to do the estimation, or how to estimate variances and confidence intervals. Identification will only tell you if you CAN estimate something, but it does not tell you HOW you can actually estimate it. If you are looking for an introduction to nonparametric ESTIMATION, you should probably look at Pagan and Ullah, which is an excellent introduction.

The book is not too technical, but it does require some prior knowledge of math and especially probability.

Rating:
Summary: Great introduction to nonparametric estimation.
Review: This book is a nonparametric introduction written for social sciences students. This implies that it's light on the mathmetical side. But the nonparametrics are easy to understand anyway. This is a highly recommended book for those who want to formalize their thoughts and want to test that model with real life data but find it difficult to apply classical parametric methods.