WMEX1400 Topics in Economics (Introductory Data Analysis for Social Science)

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Economics Program

Lecture Code WMEX1400 Subject Classification Specialized Education

Subject Name 経済学特講（社会科学のためのデータ分析入門）

Subject Name
（Katakana）ケイザイガクトクコウ（シャカイカガクノタメノデータブンセキニュウモン）

Subject Name in
English Topics in Economics (Introductory Data Analysis for Social Science)

Instructor TOMIOKA KAZUKI

Instructor
(Katakana) トミオカ　カズキ

Campus Higashi-Hiroshima Semester/Term 1st-Year, Second Semester, 3Term

Days, Periods, and Classrooms (3T) Weds9,Thur10：ECON B253, (3T) Weds10,Thur9

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (simultaneous interactive)

In principle, classes will be conducted in person. However, depending on circumstances, the course may be delivered online (remotely).

Credits 2.0 Class Hours/Week 4 Language of Instruction B : Japanese／English

Course Level 6 : Graduate Advanced

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 03 : Economics

Eligible Students Master’s students

Keywords Probability theory, Mathematical statistics, Causal inference

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline By the end of the course, students should be able to:
• use the core concepts of probability theory underlying econometrics, including probability spaces, random variables, independence, conditional expectation, and convergence
• formulate the assumptions behind major causal inference designs in probabilistic terms
• explain and assess the main empirical models used in applied microeconometrics, including experiments, regression adjustment, matching and propensity scores, fixed effects and DiD, synthetic controls, IV and LATE, and RD
• critically read and present leading papers in causal inference and applied microeconometrics
• understand why rigorous probability theory is crucial for causal inference

Class Schedule lesson1　Overview: the identification problem and potential outcomes
lesson2　Probability spaces, random variables, and distributions
lesson3　Conditional probability, Bayes’ formula, independence and conditional independence
lesson4　Expectation, conditional expectation as an L2 projection, and regression
lesson5　Convergence, the law of large numbers, the central limit theorem, estimation and inference
lesson6　Randomized experiments
lesson7　Selection on observables: subclassification, matching, the propensity score, and regression
lesson8　Panel designs: fixed effects, differences-in-differences, event studies, and synthetic control
lesson9　Instrumental variables, two-stage least squares, and the local average treatment effect
lesson10　Regression discontinuity, sharp and fuzzy
lesson11　Student presentations (1)
lesson12　Student presentations (2)
lesson13　Student presentations (3)
lesson14　Student presentations (4)
lesson15　Summary: review for the final examination

• Lecture 1 states the fundamental problem of causal inference and the meaning of identification.
• Lectures 2 to 5 cover probability theory for (applied) econometricians, about forty percent of the course.
• Lectures 6 to 10 cover the causal designs, about sixty percent of the course. Most of its lecture time is spent on published studies and their data.
• Lectures 2 to 10 are delivered in the first five weeks of the term, so that students have the second half of the term to prepare their presentations and final reports.
• Lectures 11 to 14 are student presentations of research papers. The paper to be presented is chosen by Lecture 10. Students who want a Lecture 11 or 12 slot choose it by Lecture 8. In addition to presenting, each student serves as discussant for one other student’s presentation and report.
• The course is designed on the assumption of approximately ten students. Enrollment is not known when this syllabus is written, so the arrangements for Lectures 11 to 14, namely the
number of presentations per session and the time allowed for each presentation and discussion, are fixed once enrollment is known and announced by Lecture 10. If enrollment is smaller than assumed, the schedule will be adjusted slightly. In every case the coursework itself is unchanged. Each student presents one paper, writes a report, and serves as discussant for one other student.
• Lecture 15 summarizes the six designs within a single probabilistic framework and reviews the material for the final examination.
• The grade for this course is based on a final examination, a presentation, a report, and service as discussant for another student’s presentation and report. The examination is an 80-minute closed-book written paper covering both probability theory and the causal designs. The report should include a replication of a paper published in a leading journal.
• The course schedule is intended as a guideline. Depending on the number of students enrolled, the pace of the class, and students’ understanding, some parts of the course content may be adjusted.

Text/Reference
Books,etc. Instead of relying on a particular textbook, the course will be conducted based on the lecture notes distributed in class.
One article is assigned, to be read in week 1: Imbens, G. W. (2024), “Causal Inference in the Social Sciences,” Annual Review of Statistics and Its Application 11, 123–152.
References:
Georgii, H.O., “Stochastics: Introduction to Probability and Statistics” (Translated from the third German edition)
Rosenthal, J.S., “A First Look at Rigorous Probability Theory”
Hansen, B.E., “Probability and Statistics for Economists”
Wasserman, L., “All of Statistics”
Stachurski, J., “A Primer in Econometrics”
White, H., “Asymptotic Theory for Econometricians”
Angrist, J.D., and Pischke, J., “Mostly Harmless Econometrics: An Empiricist’s Companion”
Cunningham, S., “Causal Inference: The Mixtape”
Imai, K., and Llaudet, E., “Data Analysis for Social Science: A Friendly and Practical Introduction”

PC or AV used in
Class,etc. Handouts, moodle

(More Details)

Learning techniques to be incorporated Discussions

Suggestions on
Preparation and
Review Students are expected to read the lecture materials or relevant papers before attending class. Presentation slides and reports should be written in LaTeX.

Requirements There are no prerequisites in principle. However, it is recommended that students have a solid background in graduate-level econometrics. Lectures 2–5 develop probability on a measure-theoretic basis, so
familiarity with elementary analysis (sets and functions, completeness of the real numbers) is desirable.
Students who wish to deepen the mathematical underpinnings of econometrics or statistics are encouraged to take or audit courses in probability theory and/or statistics from other departments.

Grading Method Final examination: 40%
Presentation: 30%
Report: 20%
Serving as discussant: 10%
The examination is closed book, 80 minutes, and covers both probability theory and the causal designs.
N.B. weights may change.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message Mathematics is the common language spoken amongst economists and econometricians. Accordingly, students may give presentations and write reports in either Japanese or English. This course aims not only
to enable students to read and understand leading papers, but also to prepare them to write an empirical paper for their master’s thesis. Therefore, students are encouraged to choose a paper for presentation and write a report closely related to their master’s thesis topic.

Other The lectures are delivered in English. Students may choose either Japanese or English for their presentations and written reports. The scheduled day and time are provisional and may change.

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2026Year	School/Graduate School	Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Economics Program
Lecture Code	WMEX1400	Subject Classification	Specialized Education
Subject Name	経済学特講（社会科学のためのデータ分析入門）
Subject Name （Katakana）	ケイザイガクトクコウ（シャカイカガクノタメノデータブンセキニュウモン）
Subject Name in English	Topics in Economics (Introductory Data Analysis for Social Science)
Instructor	TOMIOKA KAZUKI
Instructor (Katakana)	トミオカ　カズキ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, Second Semester, 3Term
Days, Periods, and Classrooms	(3T) Weds9,Thur10：ECON B253, (3T) Weds10,Thur9
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)
In principle, classes will be conducted in person. However, depending on circumstances, the course may be delivered online (remotely).
Credits	2.0	Class Hours/Week	4	Language of Instruction	B : Japanese／English
Course Level	6 : Graduate Advanced
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	03 : Economics
Eligible Students	Master’s students
Keywords	Probability theory, Mathematical statistics, Causal inference
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	By the end of the course, students should be able to: • use the core concepts of probability theory underlying econometrics, including probability spaces, random variables, independence, conditional expectation, and convergence • formulate the assumptions behind major causal inference designs in probabilistic terms • explain and assess the main empirical models used in applied microeconometrics, including experiments, regression adjustment, matching and propensity scores, fixed effects and DiD, synthetic controls, IV and LATE, and RD • critically read and present leading papers in causal inference and applied microeconometrics • understand why rigorous probability theory is crucial for causal inference
Class Schedule	lesson1　Overview: the identification problem and potential outcomes lesson2　Probability spaces, random variables, and distributions lesson3　Conditional probability, Bayes’ formula, independence and conditional independence lesson4　Expectation, conditional expectation as an L2 projection, and regression lesson5　Convergence, the law of large numbers, the central limit theorem, estimation and inference lesson6　Randomized experiments lesson7　Selection on observables: subclassification, matching, the propensity score, and regression lesson8　Panel designs: fixed effects, differences-in-differences, event studies, and synthetic control lesson9　Instrumental variables, two-stage least squares, and the local average treatment effect lesson10　Regression discontinuity, sharp and fuzzy lesson11　Student presentations (1) lesson12　Student presentations (2) lesson13　Student presentations (3) lesson14　Student presentations (4) lesson15　Summary: review for the final examination • Lecture 1 states the fundamental problem of causal inference and the meaning of identification. • Lectures 2 to 5 cover probability theory for (applied) econometricians, about forty percent of the course. • Lectures 6 to 10 cover the causal designs, about sixty percent of the course. Most of its lecture time is spent on published studies and their data. • Lectures 2 to 10 are delivered in the first five weeks of the term, so that students have the second half of the term to prepare their presentations and final reports. • Lectures 11 to 14 are student presentations of research papers. The paper to be presented is chosen by Lecture 10. Students who want a Lecture 11 or 12 slot choose it by Lecture 8. In addition to presenting, each student serves as discussant for one other student’s presentation and report. • The course is designed on the assumption of approximately ten students. Enrollment is not known when this syllabus is written, so the arrangements for Lectures 11 to 14, namely the number of presentations per session and the time allowed for each presentation and discussion, are fixed once enrollment is known and announced by Lecture 10. If enrollment is smaller than assumed, the schedule will be adjusted slightly. In every case the coursework itself is unchanged. Each student presents one paper, writes a report, and serves as discussant for one other student. • Lecture 15 summarizes the six designs within a single probabilistic framework and reviews the material for the final examination. • The grade for this course is based on a final examination, a presentation, a report, and service as discussant for another student’s presentation and report. The examination is an 80-minute closed-book written paper covering both probability theory and the causal designs. The report should include a replication of a paper published in a leading journal. • The course schedule is intended as a guideline. Depending on the number of students enrolled, the pace of the class, and students’ understanding, some parts of the course content may be adjusted.
Text/Reference Books,etc.	Instead of relying on a particular textbook, the course will be conducted based on the lecture notes distributed in class. One article is assigned, to be read in week 1: Imbens, G. W. (2024), “Causal Inference in the Social Sciences,” Annual Review of Statistics and Its Application 11, 123–152. References: Georgii, H.O., “Stochastics: Introduction to Probability and Statistics” (Translated from the third German edition) Rosenthal, J.S., “A First Look at Rigorous Probability Theory” Hansen, B.E., “Probability and Statistics for Economists” Wasserman, L., “All of Statistics” Stachurski, J., “A Primer in Econometrics” White, H., “Asymptotic Theory for Econometricians” Angrist, J.D., and Pischke, J., “Mostly Harmless Econometrics: An Empiricist’s Companion” Cunningham, S., “Causal Inference: The Mixtape” Imai, K., and Llaudet, E., “Data Analysis for Social Science: A Friendly and Practical Introduction”
PC or AV used in Class,etc.	Handouts, moodle
(More Details)
Learning techniques to be incorporated	Discussions
Suggestions on Preparation and Review	Students are expected to read the lecture materials or relevant papers before attending class. Presentation slides and reports should be written in LaTeX.
Requirements	There are no prerequisites in principle. However, it is recommended that students have a solid background in graduate-level econometrics. Lectures 2–5 develop probability on a measure-theoretic basis, so familiarity with elementary analysis (sets and functions, completeness of the real numbers) is desirable. Students who wish to deepen the mathematical underpinnings of econometrics or statistics are encouraged to take or audit courses in probability theory and/or statistics from other departments.
Grading Method	Final examination: 40% Presentation: 30% Report: 20% Serving as discussant: 10% The examination is closed book, 80 minutes, and covers both probability theory and the causal designs. N.B. weights may change.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	Mathematics is the common language spoken amongst economists and econometricians. Accordingly, students may give presentations and write reports in either Japanese or English. This course aims not only to enable students to read and understand leading papers, but also to prepare them to write an empirical paper for their master’s thesis. Therefore, students are encouraged to choose a paper for presentation and write a report closely related to their master’s thesis topic.
Other	The lectures are delivered in English. Students may choose either Japanese or English for their presentations and written reports. The scheduled day and time are provisional and may change.
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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