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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 Kue

14.388_r

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.

agec652_example_repo

cardoso_and_dahis_2024

causalml-teaching_mcknaus

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

demand-estimation

Demand Estimation taught by Jeff Gortmaker and Ariel Pakes

dscardoso.github.io

Diego's personal academic page, forked from academicpages/academicpages.github.io

econometrics_creel

Econometrics lecture notes with examples using the Julia language

eiadata

R Wrapper for the Energy Information Administration (EIA) API

just-the-docs

A modern, high customizable, responsive Jekyll theme for documention with built-in search.

lab-manual

mgtecon634_r

This tutorial will introduce key concepts in machine learning-based causal inference. This tutorial is used by professor Susan Athey in the MGTECON 634 at Stanford.

paper_template

Template repository for research papers.

text_algorithms_econ