Pinned Repositories
academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
BLP_Algos
various codes for BLP
cornell-cs5785-applied-ml
Teaching materials for the applied machine learning course at Cornell Tech
covid19_python
covid19_restaurant
EC421S19
Introduction to Econometrics at the University of Oregon (EC421) during Winter quarter, 2019. Taught by Ed Rubin
EC421W19
Introduction to Econometrics at the University of Oregon (EC421) during Winter quarter, 2019. Taught by Edward Rubin
econ452-course
Intro Econometrics
EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
econometrics
The template repository for the econ452 course on Learning Lab.
jhkoh17's Repositories
jhkoh17/academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
jhkoh17/BLP_Algos
various codes for BLP
jhkoh17/cornell-cs5785-applied-ml
Teaching materials for the applied machine learning course at Cornell Tech
jhkoh17/covid19_python
jhkoh17/covid19_restaurant
jhkoh17/EC421S19
Introduction to Econometrics at the University of Oregon (EC421) during Winter quarter, 2019. Taught by Ed Rubin
jhkoh17/EC421W19
Introduction to Econometrics at the University of Oregon (EC421) during Winter quarter, 2019. Taught by Edward Rubin
jhkoh17/econ452-course
Intro Econometrics
jhkoh17/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
jhkoh17/econometrics
The template repository for the econ452 course on Learning Lab.
jhkoh17/geocoding-and-web-scraping
jhkoh17/Grad-IO
Graduate Empirical Industrial Organization
jhkoh17/jhkoh17.github.io
Personal Web
jhkoh17/Machine-Learning-Notebooks
Machine Learning notebooks for refreshing concepts.
jhkoh17/mergersim
Merger simulation in Stata with nested logit demand (from 'bjornerstedt / mergersim')
jhkoh17/MMC
Multimarket Contact
jhkoh17/MMC1
jhkoh17/MMC_project
An R Markdown website template for a lab journal
jhkoh17/pandoc-templates
Templates for pandoc, tagged to release
jhkoh17/pyblp
BLP Demand Estimation with Python
jhkoh17/python-cheatsheet
Basic Cheat Sheet for Python (PDF, Markdown and Jupyter Notebook)
jhkoh17/python_basic
python_learning
jhkoh17/ResEcon703
Topics in Advanced Econometrics (ResEcon 703). University of Massachusetts Amherst. Taught by Matt Woerman
jhkoh17/svm-r-markdown-templates
This is my (deprecated) suite of R Markdown templates for academic manuscripts, beamer presentations, and syllabi. DOWNLOAD {stevetemplates} INSTEAD.
jhkoh17/test