MPE_BADA Biostatistics and Data Analysis

Faculty of Economics and Administration
Spring 2026
Extent and Intensity
2/0/0. 6 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
Ing. Michal Kvasnička, Ph.D. (lecturer)
doc. Ing. Štěpán Mikula, Ph.D. (lecturer)
Daniel Vasconcellos Archer Duque, PhD (lecturer)
Mgr. Eva Skočíková (assistant)
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
Timetable
Thu 10:00–11:50 P102, except Thu 19. 2., except Thu 9. 4.
Prerequisites
(! MPE_APIS Applied id. strategies ) && (!NOWANY( MPE_APIS Applied id. strategies ))
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
Abstract
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.
Key topics
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
Study resources and 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. info
Approaches, practices, and methods used in teaching
The course will cover the general concepts through lectures and case studies.
Method of verifying learning outcomes and course completion requirements
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
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
  • Enrolment Statistics (recent)
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