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) Inte

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 　 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, DiD, synthetic controls, IV, 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 of causal inference
lesson2　Sigma-algebras and probability spaces
lesson3　Probability measures, random variables, and distributions
lesson4　Independence, conditional probability and Bayes rule
lesson5　Expectation and moments of random variables
lesson6　Conditional expectation and conditional independence
lesson7　Inequalities and convergence of sequences of random variables
lesson8　Probability toolkit for causal designs
lesson9　Randomized experiments: Student presentation
lesson10　Regression adjustment: Student presentation
lesson11　Matching / propensity score: Student presentation
lesson12　Fixed effects / DiD: Student presentation
lesson13　Synthetic controls: Student presentation
lesson14　IV / 2SLS / LATE: Student presentation
lesson15　Regression discontinuity: Student presentation

Text/Reference
Books,etc. Instead of relying on a particular textbook, the course will be conducted based on the lecture notes distributed in class.
References:
　• Wasserman, L., “All of Statistics”
　• Stachurski, J., “A Primer in Econometrics”
　• Georgii, H.O., “Stochastics: Introduction to Probability and Statistics” (Translated from the third German edition)
　• Amemiya, T., “Introduction to Statistics and Econometrics”
　• White, H., “Asymptotic Theory for Econometricians”
　• Cunningham, S., “Causal Inference: The Mixtape”
　• Imai, K., and Llaudet, E., “Data Analysis for Social Science: A Friendly and Practical Introduction”
　• Angrist, J.D., and Pischke, J., “Mostly Harmless Econometrics: An Empiricist’s Companion”

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. In addition, they should prepare for their presentations. 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. 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 Presentation: approximately 50%
Report: approximately 50%
N.B. weights on homework assignments and exam 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) Inte
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		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, DiD, synthetic controls, IV, 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 of causal inference lesson2　Sigma-algebras and probability spaces lesson3　Probability measures, random variables, and distributions lesson4　Independence, conditional probability and Bayes rule lesson5　Expectation and moments of random variables lesson6　Conditional expectation and conditional independence lesson7　Inequalities and convergence of sequences of random variables lesson8　Probability toolkit for causal designs lesson9　Randomized experiments: Student presentation lesson10　Regression adjustment: Student presentation lesson11　Matching / propensity score: Student presentation lesson12　Fixed effects / DiD: Student presentation lesson13　Synthetic controls: Student presentation lesson14　IV / 2SLS / LATE: Student presentation lesson15　Regression discontinuity: Student presentation
Text/Reference Books,etc.	Instead of relying on a particular textbook, the course will be conducted based on the lecture notes distributed in class. References: 　• Wasserman, L., “All of Statistics” 　• Stachurski, J., “A Primer in Econometrics” 　• Georgii, H.O., “Stochastics: Introduction to Probability and Statistics” (Translated from the third German edition) 　• Amemiya, T., “Introduction to Statistics and Econometrics” 　• White, H., “Asymptotic Theory for Econometricians” 　• Cunningham, S., “Causal Inference: The Mixtape” 　• Imai, K., and Llaudet, E., “Data Analysis for Social Science: A Friendly and Practical Introduction” 　• Angrist, J.D., and Pischke, J., “Mostly Harmless Econometrics: An Empiricist’s Companion”
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. In addition, they should prepare for their presentations. 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. 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	Presentation: approximately 50% Report: approximately 50% N.B. weights on homework assignments and exam 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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