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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 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.

This book?is about research design, specifically concerning research that uses non-experimental data to figure out whether one thing causes another.?It is separated into two halves, each with different approaches to that subject. Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data.

정보제공 :

Introduction   Finding Stuff in this Book   Part I. The Design of Research   1 Designing Research   2 Research Questions   3 Describing Variables   4 Describing Relationships   5 Identification   6 Causal Diagrams   7 Drawing Causal Diagrams   8 Causal Paths and Closing Back Doors   9 Finding Front Doors   10 Treatment Effects   11 Causality with Less Modeling   Part II. The Toolbox   12 Opening the Toolbox   13 Regression   14 Matching   15 Simulation   16 Fixed Effects   17 Event Studies   18 Difference-in-Differences   19 Instrumental Variables   20 Regression Discontinuity   21 Partial Identification   22 A Gallery of Rogues: Other Methods   23 Under the Rug   Bibliography   Index