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628-module1-group4

a Github repository that contains all of the data analysis in module1 of stat628

alibi

Algorithms for monitoring and explaining machine learning models

awesome-causality-algorithms

An index of algorithms for learning causality with data

causal-curve

A python package with tools to perform causal inference using observational data when the treatment of interest is continuous.

causaldiscoverytoolbox

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

causality

Tools for causal analysis

causallift

CausalLift: Causality-based Uplift Modeling in real-world business

causalml

Uplift modeling and causal inference with machine learning algorithms

cevae

Causal Effect Inference with Deep Latent-Variable Models

cgnn

Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"

deepctr

Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

deepiv

Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction

dice

Generate Diverse Counterfactual Explanations for any machine learning model.

dml

Double Machine Learning for approximately unbiased inference

doubleml-for-py

DoubleML - Double Machine Learning in Python

elasticdl

Kubernetes-native Deep Learning Framework

grf

Generalized Random Forests

interpret

Fit interpretable models. Explain blackbox machine learning.

keras-seq-2-seq-signal-prediction

An implementation of a sequence to sequence neural network using an encoder-decoder

keras-tcn

Keras Temporal Convolutional Network.

lime

Lime: Explaining the predictions of any machine learning classifier

maml

Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

mmhc-python

An implementation of MMHC in python

pcalg-py

Implement PC algorithm in Python | PC 算法的 Python 实现

prototypical-networks-tf

Implementation of Prototypical Networks for Few-shot Learning in TensorFlow 2.0

pylift

Uplift modeling package.

scikit-learn

A version of scikit-learn that includes implementations of Wager & Athey and Scott Powers causal forests.

shap

A game theoretic approach to explain the output of any machine learning model.

stat479-machine-learning-fs18

Course material for STAT 479: Machine Learning (FS 2018) at University Wisconsin-Madison

stat628_module2