rflperry'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.
skorch-dev/skorch
A scikit-learn compatible neural network library that wraps PyTorch
rasbt/mlxtend
A library of extension and helper modules for Python's data analysis and machine learning libraries.
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.
adambielski/siamese-triplet
Siamese and triplet networks with online pair/triplet mining in PyTorch
pgmpy/pgmpy
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
paperswithcode/releasing-research-code
Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)
benedekrozemberczki/awesome-decision-tree-papers
A collection of research papers on decision, classification and regression trees with implementations.
py-why/causal-learn
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
FenTechSolutions/CausalDiscoveryToolbox
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
gpleiss/temperature_scaling
A simple way to calibrate your neural network.
LineaLabs/lineapy
Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code.
pnkraemer/tueplots
Figure sizes, font sizes, fonts, and more configurations at minimal overhead. Fix your journal papers, conference proceedings, and other scientific publications.
fatiando/verde
Processing and gridding spatial data, machine-learning style
mpastell/Pweave
Pweave is a scientific report generator and a literate programming tool for Python. It can capture the results and plots from data analysis and works well with numpy, scipy and matplotlib.
Data4Democracy/read-this-first
Start here!
logangraham/arXausality
A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.
djsutherland/arxiv-collector
A little Python script to collect LaTeX sources for upload to the arXiv.
Quantco/glum
High performance Python GLMs with all the features!
dpressel/mint
MinT: Minimal Transformer Library and Tutorials
neurodata/hyppo
Python package for multivariate hypothesis testing
max-andr/relu_networks_overconfident
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem [CVPR 2019, oral]
markus93/NN_calibration
Calibration of Convolutional Neural Networks
executablebooks/cookiecutter-jupyter-book
Cookiecutter template for a simple jupyter book
jmkuebler/auto-tst
AutoML Two-Sample Test
phcanalytics/covid19_epi_model
Code and methods for COVID19 lagged mobility and sociodemographic association model US county-level
neurodata/nd_ed
Collection of educational resources. Primarily focused on skills that are helpful in the NeuroData lab or the Neuro Data Design course at Johns Hopkins University.
neurodata/honest-forests
Honest decision forests and trees implemented efficiently and scikit-learn compliant.
evijit/SIGIR_FairRanking_UncertainInference
This repo contains code and data to reproduce the results and plots of the SIGIR 2021 paper "When Fair Ranking Meets Uncertain Inference" by Avijit Ghosh, Ritam Dutt and Christo Wilson
mcandocia/settlers_ai
reinforcement learning AI for settlers of catan