Alexander Quispe

About me

I am a Ph.D. student in Computing and Mathematical Sciences (CMS) at Caltech. I recently completed my M.S. in Computer Science at Cornell University, and prior to that, I was a Researcher at the World Bank Data Lab (2020–2025). I collaborate with Professors Victor Chernozhukov, Kate Vyborny, and Pedro Sant'Anna on developing econometric packages focused on causal machine learning.

I earned my B.S. in Economics from PUCP and an M.S. in Quantitative Economics from the University of Munich. I was also a visiting researcher at the Laboratory for Innovation Science at Harvard (LISH). I have also worked as a Research Assistant at Yale, the Max Planck Institute for Innovation and Competition (MPI), and MIT Sloan.

Please refer to my CV for more details.

Working Papers

• Impact of the Availability of ChatGPT on Software Development: A Synthetic Difference in Differences Estimation using GitHub Data
  Alexander Quispe, Rodrigo Grijalba; 2023
  [Arxiv]
• Can Market Competition Reduce Public Sector Corruption?
  Muhammad Haseeb, Amen Jalal, Alexander Quispe, Kate Vyborny; 2023
  [Draft under request]
• High Dimensional Metrics in Julia
  Alexander Quispe, Victor Chernozhukov, Christian Hansen, Martin Spindler; 2022
  [Paper]
• Fertility and Education Patterns Across Different Phases of Development
  Alexander Quispe; 2020
  [Paper]

Causal ML Packages

• csdid - Tools for computing average treatment effect parameters in Difference-in-Differences, including Double Robust Estimation.
• DRDIDpy - Implements estimators for the ATT in DiD where the parallel trends assumption holds after conditioning on pre-treatment covariates.
• TreatmentEffectRisk - Tools for bounds and inference of treatment effect risk.
• synthdid.py, synthdid.jl - Python and Julia implementation of the Synthetic Difference-in-Differences method based on Athey et al. (2021).
• HDMjl - Julia package for estimation methods in high-dimensional models based on Chernozhukov et al. (2016).
• Sensemakr.jl - Julia package for sensitivity analysis tools based on Cinelli et al. (2020).

Open Source Projects

Books and Teaching Resources

• Inference on Causal and Structural Parameters using ML and AI
  Used in course 13.28 at MIT
  [R] [Python] [Julia]
• Machine Learning and Causal Inference
  Used in course MGTECON-634 at Stanford
  [R] [Python]

AI Projects

• ILLA - AI-trained virtual assistant skilled in identifying potential obstetric and gynecological violence [Media]
• DigitalPillarAI - AI tool integrated with GPT-4 to classify World Bank Project Appraisal Documents into six pillars
• llm4tesis - AI tool integrated with GPT-4 to generate research questions from PUCP Economics thesis repository

Teaching

• Causal Inference and Machine Learning
  Graduate course, Universidad del Pacífico, 2024
  [GitHub Repository] [Students]
  Graduate course, Pontificia Universidad Católica del Perú (PUCP), Department of Economics, 2021
  [GitHub Repository] [Students]
• Python Intensive Course
  Graduate course, Laboratory of Artificial Intelligence and Computational Methods in Social Sciences, PUCP, 2021
  [GitHub Repository] [Students]
• Python Bootcamp
  Course, The Code Blinders, 2021
  [GitHub Repository]

Research Team

• Current Research Assistants
  Karl Janampa
• Alumni
  Rodrigo Grijalba (Predoc-Princeton), Jhon Flores (LSE), Valeria Albarracin, Franco Cáceres, Sandra Martinez (Columbia), Roberto Mendoza (Predoc-Princeton), Anzoni Quispe (Predoc-Princeton), Yoseph Ayala (RA-Columbia), Elibeth Cirilo (University of Warwick), Jesus Soto (Data Scientist, Interbank)