econometrics-heterogeneity

KO | EN

gitlite — search

#typescript #ai-agents #ai #dsh-plugin #deepseek-harness #open-source #claude-code #codex #cli #developer-tools #react #windows

econometrics-heterogeneity

★ 43 Open GitHub ↗

Causal Inference in Observational Data with Unobserved Heterogeneity (Lecture Notes. Masters/PhD-level)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Econometrics with Unobserved Heterogeneity – Lecture Notes

![Course header image](https://raw.githubusercontent.com/vladislav-morozov/econometrics-heterogeneity/main/src/figures/ecm_htg.png)

## Short Description

[![DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.15459848-gold)](https://doi.org/10.5281/zenodo.15459848)

This repository contains lecture notes for the course *Econometrics with Unobserved Heterogeneity*, based on a graduate-level topics course I taught at the University of Bonn.

📖 **Read the lecture notes on my website:** <https://vladislav-morozov.eu/econometrics-heterogeneity/>

Causal inference with unobserved heterogeneity is the core challenge of empirical economics. Whether due to heterogeneous treatment effects, omitted confounders, or measurement error, unobserved factors create confounding in the data and require developing appropriate methods. This course surveys some modern identification strategies and estimation methods designed to recover causal relationships from observational data, where treatments are endogenously selected and key determinants of outcomes are hidden.

## Course Overview

**Instructor:** Vladislav Morozov\
**DOI**: 10.5281/zenodo.15459848\
**Level:** Second-year Master’s and PhD students; accessible to sufficiently prepared upper-level undergraduate students

The course is structured into three main parts:

- Linear models with heterogeneous coefficients.
- Nonparametric models with unobserved heterogeneity.
- Quantile and distributional regression.

![Selected image: visual representation of distributional treatment effects (DTEs) and quantile treatment effects (QTEs)](https://raw.githubusercontent.com/vladislav-morozov/econometrics-heterogeneity/main/src/figures/dte_qte.png)
 *Selected image: visual representation of distributional treatment effects (DTEs) and quantile treatment effects (QTEs)*

## Learning Outcomes

By the end of this course, students will be able to:

- Identify and explain different sources of unobserved heterogeneity in economic data.
- Apply econometric methods, such as heterogeneous coefficient models and quantile regression, to account for unobserved heterogeneity.
- Evaluate the trade-offs and assumptions underlying different modeling approaches in empirical research.

## About These Notes, Feedback & Contributions

If you find any typos or have suggestions, please open an issue here on GitHub! Suggestions, typo reports, and comments are welcome!

## License

These lecture notes are released under the MIT License. Feel free to cite or share this material with attribution. For academic or teaching use, linking to the online version is recommended!

econometrics-heterogeneity

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README