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adv-r

Advanced R: a book

advanced-r-solutions

Set of solutions for the Advanced R programming book

ag_risk

ark-invest-tesla-valuation-model

An excerpt from our financial valuation model of Tesla

asdfree

analyze survey data for free

blogdown

Create Blogs and Websites with R Markdown

bloomberg-ml-course

Solutions to machine learning HW from bloomberg ml course

bookdown

Authoring Books and Technical Documents with R Markdown

boyuan_zhang

c_2020_chamberlain

causalml-teaching

This repository consolidates my teaching material for "Causal Machine Learning".

causaltree

Working repository for Causal Tree and extensions

code-horizon-markdown_course_materials

Course Materials for Code Horizons Course on Creating Reproducible Reports with R Markdown

comments_2024_aea

Comments on *Like Mother, Like Child: The Earned Income Tax Credit and Gender Norms*

convex_prog_in_econometrics

This is the accompanying repository for "Two Examples of Convex-Programming-Based High-Dimensional Econometric Estimators"

cookie-cutter-research-project

An example repo for a research project

course-materials

Advanced Data Analytics in Economics

course-site

Course site for Computing for the Social Sciences (MACS-30500)

course-spring2019

Machine Learning for Economists, Spring 2019

ctlatetest

Code for "Instrument Validity Tests with Causal Trees" MEA DP 1872 http://www.mea.mpisoc.mpg.de/uploads/user_mea_discussionpapers/1872_MEA_DP_05_2018.pdf

data_visualization

Code Horizon Course on Data Visualization

dmlmt

Double Machine Learning for Multiple Treatments

dse2021_tutorials

ec-607-computational-economics

ec525s19

econ3818_f2021

Kyle Butts: Introduction to Statistics with Computer Applications

econ5121a

Econ5121A: Econometric Theory and Applications at CUHK. This is an open-source writing project.

econ5170

Econ 5170 @CUHK: Computational Methods in Economics (2017 Spring)

econ622_2019

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.