MPE_BADA Biostatistics and Data Analysis

Faculty of Economics and Administration
Spring 2025

The course is not taught in Spring 2025

Extent and Intensity
2/0/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
Ing. Michal Kvasnička, Ph.D. (lecturer)
doc. Ing. Štěpán Mikula, Ph.D. (lecturer)
Guaranteed by
doc. Ing. Štěpán Mikula, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics – Faculty of Economics and Administration
Prerequisites
The course requires a basic knowledge of econometrics and some experience with using the open-source software R and RStudio. Students should be familiar with basic data structures (vector, data.frame/tibble), regression analysis (formulas and lm() function), the OLS estimator, and hypothesis testing. Any course of econometrics should provide sufficient background.
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
This course teaches statistical methods and research designs for identifying causal effects in applied fields such as microeconomics, biostatistics, and public policy evaluation. Topics will include analysis of labor markets, health care, education, and more. Through practical examples, students will understand practical applications of these techniques to real data using the statistical software R.
Learning outcomes
Upon completion of the course, students will have the ability to: a. Recognize the significance of experiments in causal inference b. Grasp key concepts of identification strategies and their application in causal inference c. Implement identification strategies in the analysis of observational data d. Evaluate and critically discuss the necessity, methods, and limitations of public policy evaluation.
Syllabus
  • The problem of policy evaluation (selection bias) • Causal inference and counterfactuals (Rubin causal model) • Randomized Assignment (experiments) • Regression analysis • Instrumental variables • Regression discontinuity design • Difference-in-differences • Matching and SCM
Literature
    required literature
  • HUNTINGTON-KLEIN. The effect: An introduction to research design and causality. Chapman and Hall/CRC, 2022. info
    recommended literature
  • CUNNINGHAM, Scott. Causal inference : the mixtape. New Haven: Yale University Press, 2021, x, 572. ISBN 9780300251685. info
  • ANGRIST, Joshua David and Jörn-Steffen PISCHKE. Mostly harmless econometrics : an empiricist's companion. Princeton: Princeton University Press, 2009, xiii, 373. ISBN 9780691120355. URL info
Teaching methods
The course will cover the general concepts through lectures and case studies.
Assessment methods
Final written exam with the minimum requirement of 60% points. Evaluation: • A: (88; 100] • B: (81; 88] • C: (74; 81] • D: (67; 74] • E: (60; 67] • F: [0, 60]
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
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