ECON 526: Quantitative Economics with Data Science Applications

Slides

1. Introduction to Causality, notebook

   • Reading: Chapter 2 of Chernozhukov et al. (2024) or Chapter 1 of Facure (2022)

2. Overview of Causal Inference Methods

3. Uncertainty Quantification, notebook

   • Chapter 3 of Facure (2022)

4. Linear regression, notebook

   • Chapters 5-7 of Facure (2022) or Chapters 1-2 of Chernozhukov et al. (2024)

5. Matching slides, notebook

   • Chapters 10-12 of Facure (2022)
   • Chapter 14 of Huntington-Klein (2021)
   • Chapters 5 and 10 of Chernozhukov et al. (2024)

6. Introduction to difference in differences, notebook

   • Reading: chapter 13 of Facure (2022)

7. Fixed Effects, notebook

   • Reading: chapter 14 of Facure (2022)

8. Advanced difference in differences, notebook

   • Reading: chapter 24 of Facure (2022), Roth et al. (2023), Chaisemartin and D’Haultfœuille (2022)

9. Instrumental Variables, notebook

   • Reading: Chapter 8 and 9 of Facure (2022)

10. Synthetic Control, notebook

    • Reading: Abadie (2021),chapter 15 of Facure (2022)

11. Regression discontinuity, notebook

12. Debiased Machine Learning, notebook

• Reading: QuantEcon: ML in Economics

11. Treatment Heterogeneity and Conditional Effects, notebook

• Reading: QuantEcon: Heterogeneity

12. Neural Networks, notebook

• Reading: QuantEcon Datascience: Regression - Neural Networks

Declaration of AI Use

These slides were originally written before AI was useful. AI is being used for corrections, code updates required by dependency updates, and some generation of new figures.

References

Abadie, Alberto. 2021. “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects.” Journal of Economic Literature 59 (2): 391–425. https://doi.org/10.1257/jel.20191450.

Chaisemartin, Clément de, and Xavier D’Haultfœuille. 2022. “Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: a survey.” The Econometrics Journal 26 (3): C1–30. https://doi.org/10.1093/ectj/utac017.

Chernozhukov, V., C. Hansen, N. Kallus, M. Spindler, and V. Syrgkanis. 2024. Applied Causal Inference Powered by ML and AI. https://causalml-book.org/.

Facure, Matheus. 2022. Causal Inference for the Brave and True. https://matheusfacure.github.io/python-causality-handbook/landing-page.html.

Huntington-Klein, Nick. 2021. The Effect: An Introduction to Research Design and Causality. CRC Press. https://theeffectbook.net/.

Roth, Jonathan, Pedro H. C. Sant’Anna, Alyssa Bilinski, and John Poe. 2023. “What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature.” Journal of Econometrics 235 (2): 2218–44. https://doi.org/https://doi.org/10.1016/j.jeconom.2023.03.008.