Alternatives and similar repositories for causal_transfer_learning:

Users that are interested in causal_transfer_learning are comparing it to the libraries listed below

• caus-am / dom_adapt
  Code for the NIPS 2018 paper "Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions"
  ☆18Updated 3 years ago
• sjblim / rmsn_nips_2018
  ☆37Updated 6 years ago
• weirayao / leap
  LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network.
  ☆35Updated 2 years ago
• mfederici / dsit
  Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"
  ☆25Updated 3 years ago
• capybaralet / REx_code_release
  ☆64Updated 4 years ago
• dmelis / SENN
  Self-Explaining Neural Networks
  ☆13Updated last year
• AMLab-Amsterdam / DataAugmentationInterventions
  ☆27Updated 4 years ago
• kakaobrain / irm-empirical-study
  An Empirical Study of Invariant Risk Minimization
  ☆27Updated 4 years ago
• juangamella / icp
  Python implementation of the Invariant Causal Prediction (ICP) algorithm, from the 2015 paper "Causal inference using invariant predictio…
  ☆20Updated last year
• juangamella / aicp
  Code to reproduce the experimental results from the paper "Active Invariant Causal Prediction: Experiment Selection Through Stability", b…
  ☆19Updated last year
• dtak / ocbnn-public
  General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.
  ☆38Updated 2 years ago
• WeijiaZhang24 / TEDVAE
  Code for TEDVAE, a VAE-based treatment effect estimation algorithm.
  ☆23Updated 2 years ago
• ahujak / IB-IRM
  ☆30Updated 3 years ago
• siplab-gt / generative-causal-explanations
  Code for "Generative causal explanations of black-box classifiers"
  ☆33Updated 4 years ago
• ElementAI / causal_discovery_toolbox
  Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
  ☆30Updated 5 years ago
• seedatnabeel / TE-CDE
  Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)
  ☆27Updated 2 years ago
• GiulsLu / OT-gradients
  ☆12Updated 6 years ago
• diviyank / SAM
  Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)
  ☆52Updated 4 years ago
• RuntianZ / doro
  Distributional and Outlier Robust Optimization (ICML 2021)
  ☆26Updated 3 years ago
• yixinwang / representation-causal-public
  ☆44Updated 3 years ago
• charmlab / recourse
  Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
  ☆36Updated 2 years ago
• bunnech / gwgan
  Learning Generative Models across Incomparable Spaces (ICML 2019)
  ☆27Updated 5 years ago
• YoungseogChung / calibrated-quantile-uq
  Training quantile models
  ☆43Updated 3 months ago
• IBM / wasserstein-barycenters
  Code for our ICLR19 paper "Wasserstein Barycenters for Model Ensembling", Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero…
  ☆22Updated 5 years ago
• christinaheinze / core
  TensorFlow implementation of 'Core', proposed in "Conditional Variance Penalties and Domain Shift Robustness".
  ☆10Updated 6 years ago
• yuachen / CausalDA
  Code to reproduce the numerical experiments in the paper Domain adaptation under structural causal models by Yuansi Chen and Peter Bühlma…
  ☆18Updated 3 years ago
• facebookresearch / InvarianceUnitTests
  Toy datasets to evaluate algorithms for domain generalization and invariance learning.
  ☆42Updated 3 years ago
• JonathanWenger / pycalib
  Non-Parametric Calibration for Classification (AISTATS 2020)
  ☆19Updated 3 years ago
• lightlightdyy / Deep-Learning-and-Causal-Inference
  ☆29Updated 6 years ago
• CausalML / DeepGMM
  ☆34Updated 5 years ago