d2cml-ai

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.

CausalAI-Course

Lectures and Tutorials for the Causal AI course

csdid

CSDID

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.

Sensemakr.jl

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

Synthdid.jl

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

synthdid.py

d2cml-ai/CausalAI-Course

Lectures and Tutorials for the Causal AI course

d2cml-ai/csdid

CSDID

d2cml-ai/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.

d2cml-ai/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.

d2cml-ai/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.

d2cml-ai/synthdid.py

d2cml-ai/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.

d2cml-ai/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.

d2cml-ai/Sensemakr.jl

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

d2cml-ai/HDMjl.jl

d2cml-ai/Synthdid.jl

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

d2cml-ai/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

d2cml-ai/DRDIDpy

d2cml-ai/JlSensemakr.jl

d2cml-ai/osrmareas

d2cml-ai/surrogates

d2cml-ai/synthdid

d2cml-ai/dvds-py

d2cml-ai/llm4tesis

d2cml-ai/wb-project-analysis

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

d2cml-ai/becas_perm

d2cml-ai/csdid-pyspark

d2cml-ai/gpt-github-sdid

d2cml-ai/hdmpy

A python port of the hdm package for R

d2cml-ai/llm4tesis-app

d2cml-ai/llm4tesis-chatbot-ui

d2cml-ai/llm4tesis-data-pipeline

d2cml-ai/llm4tesis-knowledge-base

d2cml-ai/streamlit-template-CAI-UP

d2cml-ai/TreatmentEffectRisk