Albert Alex Zevelev
My interests include Household Finance, Housing, and Macro Finance. I work on methods that exploit non-causal machine learning techniques to improve causal inference.
- New York, New York
- Baruch College
- arXiv
- Github
- Google Scholar
- Impactstory
- ORCID
- ResearchGate
- SSRN
- Stackoverflow
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Future Post Machine Learning for Causal Inference: Synthetic Control and Double Machine Learning
less than 1 minute read
Published:
This post is inspired by Frank Diebold’s
Future Post Random Variables in Julia compared to MATLAB/R/STATA/Mathematica/Python
3 minute read
Published:
This post compares the way random variables are handled in Julia/MATLAB/R/STATA/Mathematica/Python. It was inspired by Bruce Hansen’s recent textbook which compares statistical commands in Matlab/R/STATA on page 114. This post will focus on the main methods for working with random variables in a language: e.g. Distributions.jl is the flagship Julia package for random variables, MATLAB’s internal distributions, Base R, Base STATA, Mathematica, and Python’s SciPy.
Simpson’s Paradox is a Special Case of Omitted Variable Bias
9 minute read
Published:
The goal of this post is to illustrate a point made in a recent tweet by Amit Ghandi that Simpson’s Paradox is a special case of omitted variable bias.
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