Case Studies And Causal Inference

A discussion of the case study method which develops an integrative framework for causal inference in small-n research. This framework is applied to research design tasks such as case selection and process tracing. The book presents the basics, state-of-the-art and arguments for improving the case study method and empirical small-n research.

A discussion of the case study method which develops an integrative framework for causal inference in small-n research. This framework is applied to research design tasks such as case selection and process tracing. The book presents the basics, state-of-the-art and arguments for improving the case study method and empirical small-n research.

An introduction to causal case study methods, complete with step-by-step guidelines and examples

An innovative and accessible textbook on multimethod and case-study research Multimethod research has become indispensable to doing social science, and is essential to anyone who conducts large-scale research projects in political science, sociology, education, comparative law, or business. This authoritative and accessible book offers the first truly comprehensive approach to multimethod and case-study research, and is particularly aimed at students of qualitative methods in the social sciences. Walking step-by-step through these cutting-edge tools and techniques, Gary Goertz introduces a new integrated approach that unites three corners of a powerful research triad—causal mechanisms, cross-case causal inference, and within-case causal inference. He explains how the investigation of causal mechanisms and the making of within-case causal inference are the central goals of multimethod and case study research, and provides a logic for connecting case studies and causal mechanism analysis with cross-case analysis, whether they are statistical analyses, experiments, or QCA. In addition, Goertz analyzes how one can generalize using case studies, as well as systematically test game-theoretic and other models using multiple case studies. Provides a fully integrated approach to multimethod and case-study research An essential resource for students and researchers in political science, sociology, education, law, and business Covers constraint causal mechanism, game theory and case studies, QCA, and the use of case studies to systematically test and generalize theories An ideal textbook for a first-year graduate course in methods or research design

In the first volume of its kind, a collection of top policy scholars combine empirical and methodological analysis in the field of comparative policy studies to provide compelling insights into the formulation, implementation and evaluation of policies across regional and national boundaries.

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

This book provides the first systematic guide to designing multi-method research, considering a wide range of statistical and qualitative tools.

Designing Social Inquiry focuses on improving qualitative research, where numerical measurement is either impossible or undesirable. What are the right questions to ask? How should you define and make inferences about causal effects? How can you avoid bias? How many cases do you need, and how should they be selected? What are the consequences of unavoidable problems in qualitative research, such as measurement error, incomplete information, or omitted variables? What are proper ways to estimate and report the uncertainty of your conclusions?