The Effect

The Effect: An Introduction to Research Design and Causality is 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.

Auteur

Nick Huntington-Klein is a professor of economics at Seattle University specializing in the study of the education system and applied econometrics. He is known as someone who can clearly explain complex topics in econometrics, and his teaching materials have been shared online tens of thousands of times. To his great anguish, his toddler daughter refuses to learn about sampling variation and would rather listen to that one Moana song on repeat.

Texte du rabat

The Effect: An Introduction to Research Design and Causality is 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? Key Features: Extensive code examples in R, Stata, and Python Chapters on overlooked topics in econometrics classes: 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

Résumé

The Effect: An Introduction to Research Design and Causality is 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?

Key Features:

• • Extensive code examples in R, Stata, and Python
• • Chapters on overlooked topics in econometrics classes: 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

Contenu

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