Pinned Repositories
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
causal_inference_python_code
Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
egen_runmax
Stata package for running max, min, and range
mata_testcase
A testing framework for Stata's Mata language
python-in-stata
Use Python within Stata
stata-dta-in-python
Use Stata .dta files in Python
stata-kernel
Stata kernel for IPython/Jupyter
stata_argmax
Stata package for finding argmax and argmin
xynn
jrfiedler's Repositories
jrfiedler/causal_inference_python_code
Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
jrfiedler/CASI_Python
Python code for Computer Age Statistical Inference
jrfiedler/causal_inference_julia_code
Julia code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
jrfiedler/python-in-stata
Use Python within Stata
jrfiedler/xynn
jrfiedler/stata-dta-in-python
Use Stata .dta files in Python
jrfiedler/stata-kernel
Stata kernel for IPython/Jupyter
jrfiedler/mata_testcase
A testing framework for Stata's Mata language
jrfiedler/egen_runmax
Stata package for running max, min, and range
jrfiedler/stata_argmax
Stata package for finding argmax and argmin
jrfiedler/StataDtaJS
Use Stata .dta files in JavaScript
jrfiedler/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
jrfiedler/jumble
Stata package for permuting observations in select data variables
jrfiedler/matatools
Tools for Stata's Mata language
jrfiedler/StataCon2014
Code used in my Stata Conference presentation
jrfiedler/tabnet
PyTorch implementation of TabNet paper
jrfiedler/DecisionTree.jl
Julia implementation of Decision Tree (CART) and Random Forest algorithms
jrfiedler/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.
jrfiedler/google-research
Google Research
jrfiedler/scikit-learn
scikit-learn: machine learning in Python
jrfiedler/statsmodels
Statsmodels: statistical modeling and econometrics in Python
jrfiedler/sympy
A computer algebra system written in pure Python