dscardoso

PATHSolver.jl

provides a Julia wrapper for the PATH Solver for solving mixed complementarity problems

Cardinal

Virtual modular synthesizer plugin

Demand-Estimation

Demand Estimation taught by Jeff Gortmaker and Ariel Pakes

FriendsDontLetFriends

Friends don't let friends make certain types of data visualization - What are they and why are they bad.

CompEconR

Porting Miranda&Fackler's CompEcon toolbox from Matlab to R

EmpiricalIO

StructEst_W20

MACS 40200 (Winter 2020): Structural Estimation

CompMethods

"Computational Methods for Economists using Python", by Richard W. Evans. Tutorials and executable code in Python for the most commonly used computational methods in economics.

blsR

BLS API V2 interface

EIAapi

Supporting tools for the Applied Time Series Analysis and Forecasting book

Diff-in-Diff

zotenote

A VSCode extension that allows you to easily create literature notes with bibliographic information from Zotero.

vscode-zotero

Zotero Better Bibtex citations for VS Code

dendron

The personal knowledge management (PKM) tool that grows as you do!

alphavantager

A lightweight R interface to the Alpha Vantage API

GenericML

R implementation of Generic Machine Learning Inference (Chernozhukov, Demirer, Duflo and Fernández-Val, 2020).

jpor_codes

Codes for the book "Julia Programming for Operations Research"

aipwML

Regression adjustment, IPW, and AIPW estimators for causal effects using various ML methods

InfiniteOpt.jl

An intuitive modeling interface for infinite-dimensional optimization problems.

prompts-for-edu

FRAC.jl

This package estimates mixed logit demand models using the fast, "robust", and approximately correct (FRAC) approach developed by Salanie and Wolak (2019).

Grumps.jl

causalHAL

Adaptive debiased machine learning of treatment effects with the highly adaptive lasso

rig

The R Installation Manager

juliaup

Julia installer and version multiplexer

text_algorithms_econ

doubleml-for-r

DoubleML - Double Machine Learning in R

paper_template

Template repository for research papers.

ACE-592-SAE

ACE 592 SAE: Data Science for Applied Economics

14.388_jl

This Jupyterbook has been created based on the tutorials of the course 14.388 Inference on Causal and Structural Parameters Using ML and AI in the Department of Economics at MIT taught by Professor Victor Chernozukhov. All the scripts were in R and we decided to translate them into Julia, so students can manage both programing languages. Jannis Kueck and V. Chernozukhov have also published the original R Codes in Kaggle. In adition, we included tutorials on Heterogenous Treatment Effects Using Causal Trees and Causal Forest from Susan Athey’s Machine Learning and Causal Inference course. We aim to add more empirical examples were the ML and CI tools can be applied using both programming languages.