takala's Stars
rgiordan/zaminfluence
Tools in R for computing and using Z-estimator approximate influence functions.
esantorella/hdfe
lmcinnes/umap
Uniform Manifold Approximation and Projection
jaanli/variational-autoencoder
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
dialnd/imbalanced-algorithms
Python-based implementations of algorithms for learning on imbalanced data.
edgeslab/CTL
A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Python
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.
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
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.
gdmarmerola/interactive-intro-rl
Big Data's open seminars: An Interactive Introduction to Reinforcement Learning
philipperemy/n-beats
Keras/Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
damitkwr/ESRNN-GPU
PyTorch GPU implementation of the ES-RNN model for time series forecasting