jrfiedler's repositories
causal_inference_python_code
Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
CASI_Python
Python code for Computer Age Statistical Inference
causal_inference_julia_code
Julia code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
python-in-stata
Use Python within Stata
stata-dta-in-python
Use Stata .dta files in Python
stata-kernel
Stata kernel for IPython/Jupyter
mata_testcase
A testing framework for Stata's Mata language
egen_runmax
Stata package for running max, min, and range
stata_argmax
Stata package for finding argmax and argmin
StataDtaJS
Use Stata .dta files in JavaScript
StataCon2014
Code used in my Stata Conference presentation
DecisionTree.jl
Julia implementation of Decision Tree (CART) and Random Forest algorithms
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.
google-research
Google Research
scikit-learn
scikit-learn: machine learning in Python
statsmodels
Statsmodels: statistical modeling and econometrics in Python