Course Overview

This course introduces students to the fundamental of causal inference that constitutes the core of quantitative social sciences today. The course is divided into three parts: (1) conceptual discussion of causal inference, (2) elementary statistical theory, and (3) programming with statistical software. We are using a free software, R, which has been widely used by both scientists and major multinational corporations including Google.

Learning Achievement

This course places emphasis on the importance of research design that comes before any application of statistical model. In other words, one of the most important objectives of this course is that students realize difficulties and challenges associated with causal inference. After this course, interested students are encouraged to take courses on statistics (e.g., BE22321) and econometrics (e.g., BE22231) to further learn different statistical techniques that are available today.

Competence

In this course, lectures proceed with data analyses examples with R. Students are expected to actively practice data analyses during the lectures in order to improve their programming skills, learn how to conduct quantitative research, and better understand causal inference. Data analyses examples are drawn from different fields of social sciences including political science, economics, and public policy. Quizzes, programing assignments, and problem sets are used to assess how well students are understanding materials covered in this course.

Course prerequisites

No course is required.

Grading Philosophy

Course grades will be based on:
- Active reading of the course textbook through Perusall (20%)
- Programming assignments (10%)
- Problem sets (30%)
- Two take-home exams (20% each)
- No extra/optional credit assignments will be offered to individual students.

Course schedule

Introduction to R
Introduction to R
Causality
Causality
Measurement
Measurement
Prediction
Prediction
Prediction

Course type

Lectures

Online Course Requirement

Instructor

Seki Katsunori

Other information

Class Attendance
Although attendance does not factor explicitly in your grade, to do well in this course it is important that you come to class regularly. Most importantly, if you miss class meetings more than three times (i.e., four times or more), your grade becomes D (Fail) regardless of your performance. This is a university rule that I will follow strictly, and therefore I will take attendance at the beginning of each class.

Site for Inquiry

Link to the syllabus provided by the university