42 Open source projects that are alternatives of or similar to cfml_tools

Machine learning based causal inference/uplift in Python

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.

Python package for the creation, manipulation, and learning of Causal DAGs

Causal Discovery for Python. Translation and extension of the Tetrad Java code.

We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.

Awesome Neural Logic and Causality: MLN, NLRL, NLM, etc. 因果推断，神经逻辑，强人工智能逻辑推理前沿领域。

This repository contains the dataset and the PyTorch implementations of the models from the paper Recognizing Emotion Cause in Conversations.

[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias

Uplift modeling and causal inference with machine learning algorithms

Code for CIKM'18 paper, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.

Must-read papers and resources related to causal inference and machine (deep) learning

Regulatory networks with Direct Information in python

A Causal Relation Schema for Text

Causal Deconvolution of Networks by Algorithmic Generative Models

Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Python3 | PyTorch | CNNs | Causality | Reasoning | LSTMs | Transformers | Multi-Head Self Attention | Published in IEEE Winter Conference on Applications of Computer Vision (WACV) 2021

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记

Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"

Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893

Library for graphical models of decision making, based on pgmpy and networkx

Code for our paper, Neural Network Attributions: A Causal Perspective (ICML 2019).

Coz: Causal Profiling

Python Library for learning (Structure and Parameter) and inference (Probabilistic and Causal) in Bayesian Networks.

Covariate Balance Tables and Plots - An R package for assessing covariate balance

Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3

Implement PC algorithm in Python | PC 算法的 Python 实现

DoubleML - Double Machine Learning in R

💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatments from observational data using neural networks.

DoubleML - Double Machine Learning in Python

Causal Inference: What If. R and Stata code for Exercises

🎓 GSU MPA/MPP course on program evaluation and causal inference

Nonparametric estimators of the average treatment effect with doubly-robust confidence intervals and hypothesis tests

Algorithms for causal inference and the detection of dynamical coupling from time series, and for approximation of the transfer operator and invariant measures.

A Python package for causal inference using Synthetic Controls

Policy learning via doubly robust empirical welfare maximization over trees

Causal Effect Inference for Structured Treatments (SIN) (NeurIPS 2021)

Causal Inference for the Brave and True的中文翻译版。全部代码基于Python，适用于计量经济学、量化社会学、策略评估等领域。英文版原作者：Matheus Facure

➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.

CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable.

🎯 📕 Targeted Learning in R: A Causal Data Science Handbook

Methods for subgroup identification / personalized medicine / individualized treatment rules

Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.

因果推理&AB实验相关论文小书库