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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 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.

14.388_py

This material 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 Python, 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.

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.

becas_perm

csdid

CSDID

csdid-pyspark

d2cml.ai

drdidpy

dvds-py

gpt-github-sdid

hdmjl.jl

hdmpy

A python port of the hdm package for R

jlsensemakr.jl

llm4tesis

llm4tesis-app

llm4tesis-chatbot-ui

llm4tesis-data-pipeline

llm4tesis-knowledge-base

mgtecon634_py

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. Scripts were translated into Python.

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.

osrmareas

python_visual_library

This is a repository maintained by D2CML and containing example graphs on how to explore data sets and display results of Impact Evaluations using Python

sensemakr.jl

Julia implementation of the original R sensemakr package: https://github.com/carloscinelli/sensemakr

streamlit-template-cai-up

surrogates

synthdid

synthdid.jl

Synthetic difference in differences - Julia implementation of https://synth-inference.github.io/synthdid/

synthdid.py

treatmenteffectrisk

wb-project-analysis

Chatbot application for analyzing documents, specially made for analyzing World Bank project documents.