hbg26's Stars
py-why/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.
jeffheaton/t81_558_deep_learning
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
py-why/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.
pyjanitor-devs/pyjanitor
Clean APIs for data cleaning. Python implementation of R package Janitor
cerlymarco/shap-hypetune
A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
amit-sharma/causal-inference-tutorial
Repository with code and slides for a tutorial on causal inference.
giuseppec/iml
iml: interpretable machine learning R package
AdrianAntico/AutoQuant
R package for automation of machine learning, forecasting, model evaluation, and model interpretation
chrisconlon/Grad-IO
Graduate Empirical Industrial Organization
atif-hassan/PyImpetus
PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in order to provide not just the best set of features but also the best combination of features
rupskygill/ML-mastery
Code from Jason Brownlee's course on mastering machine learning
atif-hassan/FRUFS
An unsupervised feature selection technique using supervised algorithms such as XGBoost
ScienceKot/kydavra
A python package for feature selection in python
evanjflack/bacondecomp
Bacon-Goodman decomposition for differences-in-differences with variation in treatment timing.
joergrieger/bvars
R package for Bayesian Vector Autoregression
deaneckles/randomization_inference
Tutorial in randomization inference, experimental design and analysis, and experiments in networks.
Vevesta/VevestaX
2 Lines of code to track ML experiments + EDA + check into Github
damiancclarke/cdifdif
Estimating Difference-in-Differences in the Presence of Spillovers -- Algorithm in Stata, R and MATLAB
osofr/tmlenet
Targeted Maximum Likelihood Estimation for Network Data
saforem2/ambivalent
Minimal, beautiful (+ highly-customizable) styles for Matplotlib.
brentzucker/brownlee
Code snippets from Jason Brownlee's ML and Deep Learning books.
kmfullerton/Deep_Learning_Time_Series
Repository for working through Jason Brownlee's Deep Learning for Time-Series Forecasting Course
SigmoidAI/kydavra
Kydavra is a python sci-kit learn inspired package for feature selection. It used some statistical methods to extract from pure pandas Data Frames the columns that are related to column that your model should predict.
sjtuhlz/Discrete-Choice-Models
Empirical Industrial Organization, structural discrete choice models implementations, conditional logit models/nested logit models/Random Coefficient logit models
brunamirelle/industrial_organization
jcwilk/creative_algorithms_ga_example
Genetic Algorithm example borrowed from Jason Brownlee's eBook, Clever Algorithms
damiancclarke/spillovers
Code and Results for the paper "Estimating Treatment Effects in the Presence of Local Spillovers"
nograpes/tmle_readmissions
Analysis of hospital readmissions using targeted maximum likelihood (TMLE)
stefan-bergstein/Deep-Learning-on-Titanic-data
Applying my basic learnings from the book Deep Learning With Python by Jason Brownlee that I recently studied. Publish my first kaggle notebook for my own practice to become a Kaggler :-)