Counterfactuals and Causal Inference
- 499 pages
- 18 hours of reading
This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.
This series delves into the empirical and formal methods crucial for social science research. It offers deep insights into the theoretical underpinnings of analytical techniques and their practical application in research. Volumes cover a broad scope across disciplines, as well as specific methodological applications in fields like political science, sociology, and public health. It serves students and researchers seeking advanced knowledge in social sciences and statistics.
This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.
This book provides an overview of cutting-edge approaches to computational social science.
This book shows how to model the spatial interactions between actors that are at the heart of the social sciences.
Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. The book covers ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting.
Intended for students in political science and economics who have already taken a course in game theory, this text provides a unified and accessible survey of canonical and important new formal models of domestic politics.
The book offers a thorough introduction to the mathematical principles essential for contemporary social scientists. It emphasizes the application of mathematical concepts to social science research, providing tools and techniques that enhance analytical skills. By bridging the gap between mathematics and social science, it equips readers with the necessary knowledge to effectively interpret data and engage in quantitative analysis.