Fundamentals of Monte Carlo Simulations in Data Science

Simulations

Course Information

Instructor: Vladislav Morozov
Email: morozov [at] uni-bonn.de
Office Location: Adenauerallee 24-42, IFS, Statistics Section
Office Hours: Virtual, by appointment
Course Website: eCampus and this website
Meeting times: Room 0.042: 14:00-16:00, Wednesdays; 08:30-10:00, Fridays
Schedule Changes and Holidays: See eCampus and BASIS

Level: master’s
Prerequisites: coursework in econometrics and statistics

Course source code: GitHub
Simulations in: Python

In Slide Form

View slides in full screen

Course Content

Motivation

How do we know if a new causal inference method actually estimates the effects of interest? Does a machine learning algorithm generalize well across different sample sizes? What happens when a statistical tool is applied outside its original setting?

Simulations provide answers to such questions. These “laboratories” of econometrics and statistics allow us to test methods in controlled environments, building confidence in their real-world applicability.

This research module focuses on the design, implementation, and interpretation of Monte Carlo simulations. It prepares students to write a rigorous master’s thesis in econometrics and statistics, while equipping them with the skills to critically evaluate methods in diverse data science contexts, applied and theoretical. The focus is mainly practical and numerical, with the in-class simulations built in Python.

Key Contents

This course covers the following points:

  • The role of simulations in evaluating statistical methods.
  • Principles of well-designed simulations:
    • Theoretical foundations
    • Best practices for implementation.
  • Context-specific applications:
    • Evaluating confidence intervals and hypothesis tests.
    • Assessing predictive algorithms.
    • Evaluating causal estimators.

This course is offered as a part of the Research Module in Econometrics and Statistics.

Course Materials

Simulations

This course is self-contained with respect to simulations, and the lecture materials serve as the primary reference. For some further references and philosophical perspectives, one may also consult

  • Dormann, Carsten F., and Aaron M. Ellison. 2025. Statistics by Simulation: A Synthetic Data Approach. Princeton University Press.

Project Resources

For the course project, students are encouraged to analyze methods at the level of modern advanced textbooks. The choice of topic is flexible, subject to feasibility. The following texts may inspire project ideas:

  • Chernozhukov, Victor, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis. 2024. Applied Causal Inference Powered by ML and AI.
  • Gaillac, Christophe, and Jeremy L’Hour. 2025. Machine Learning for Econometrics. Oxford University Press.
  • Wager, Stefan. 2024. Causal Inference: A Statistical Learning Approach.

Resources on Writing and Presenting

The course project requires students to document their findings in a term paper and present their results effectively. The following resources provide guidance on scientific writing and presentation skills:

  • Alley, Michael. 2013. The Craft of Scientific Presentations: Critical Steps to Succeeed and Critical Errors to Avoid. 2. ed. Springer.
  • Nikolov, Plamen. 2022. Writing Tips for Economics Research Papers: 2021-2022 Edition. https://doi.org/10.2139/ssrn.4114601
  • Schimel, Joshua, ed. 2012. Writing Science: How to Write Papers That Get Cited and Proposals That Get Funded. Oxford University Press.

Course Policies

Grading and Evaluation

The course grade is based on the course project. Students will create, run, and evaluate simulations for a statistical method of their choice. Grading criteria include:

  • Rigorous interpretation and analysis of the simulation results,
  • Organization, replicability, and documentation of the simulation code,
  • Clarity of the public presentation and submitted project text.

See the course project page for more detailed information.

Policies and Additional Information

Attendance and Participation:
Regular attendance and active participation are strongly encouraged.

Academic Integrity:
Students must adhere to the university’s policies on academic integrity and plagiarism. Any violations will be subject to disciplinary action.

Accommodations:
If you require any accommodations due to a disability or other circumstances, please contact the relevant office as soon as possible.