Nick Huntington-Klein – författare

The Effect: An Introduction to Research Design and Causality, Second edition is an excellent teaching text about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject. The first half goes through the concepts of causality, with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a causal interpretation. Subjects include heavy emphasis on data-generating processes and causal diagrams.Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we “add a control variable” what does that actually do?The target audience is practitioners as well as undergraduate and graduate students studying causal inference in various fields such as statistics, econometrics, biostatistics, the social sciences and data science. Key Features:Extensive code examples in R, Stata, and PythonChapters on heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptionsAn easy-to-read conversational toneUp-to-date coverage of methods with fast-moving literatures like difference-in-differencesThe second edition features a new chapter on partial identification, updated materials, methods, and writing throughout, and additional materials for help navigating the book or in using the book in teaching.

The Effect: An Introduction to Research Design and Causality, Second edition is an excellent teaching text about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject. The first half goes through the concepts of causality, with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a causal interpretation. Subjects include heavy emphasis on data-generating processes and causal diagrams.Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we “add a control variable” what does that actually do?The target audience is practitioners as well as undergraduate and graduate students studying causal inference in various fields such as statistics, econometrics, biostatistics, the social sciences and data science. Key Features:Extensive code examples in R, Stata, and PythonChapters on heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptionsAn easy-to-read conversational toneUp-to-date coverage of methods with fast-moving literatures like difference-in-differencesThe second edition features a new chapter on partial identification, updated materials, methods, and writing throughout, and additional materials for help navigating the book or in using the book in teaching.

The Effect: An Introduction to Research Design and Causality, Second edition is an excellent teaching text about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject. The first half goes through the concepts of causality, with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a causal interpretation. Subjects include heavy emphasis on data-generating processes and causal diagrams.Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we "e;add a control variable"e; what does that actually do?The target audience is practitioners as well as undergraduate and graduate students studying causal inference in various fields such as statistics, econometrics, biostatistics, the social sciences and data science. Key Features: Extensive code examples in R, Stata, and Python Chapters on heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions An easy-to-read conversational tone Up-to-date coverage of methods with fast-moving literatures like difference-in-differences The second edition features a new chapter on partial identification, updated materials, methods, and writing throughout, and additional materials for help navigating the book or in using the book in teaching.