Alternatives and similar repositories for ucate

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

• diviyank / SAM
  Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)
  ☆54Updated 4 years ago
• nke001 / causal_learning_unknown_interventions
  Code for "Neural causal learning from unknown interventions"
  ☆105Updated 5 years ago
• wjmaddox / drbayes
  Code Repo for "Subspace Inference for Bayesian Deep Learning"
  ☆83Updated last year
• jsyoon0823 / INVASE
  Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR
  ☆64Updated 5 years ago
• maxwshen / iap-cidl
  Causal Inference & Deep Learning, MIT IAP 2018
  ☆89Updated 7 years ago
• lightlightdyy / Deep-Learning-and-Causal-Inference
  ☆30Updated 7 years ago
• iancovert / removal-explanations
  A lightweight implementation of removal-based explanations for ML models.
  ☆59Updated 4 years ago
• sfme / RVAE_MixedTypes
  Repository for code release of paper "Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data" (AISTATS 2020)
  ☆50Updated 5 years ago
• aangelopoulos / rcps
  Official codebase for "Distribution-Free, Risk-Controlling Prediction Sets"
  ☆87Updated last year
• microsoft / EDDI
  Code for paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
  ☆41Updated 2 years ago
• OATML / causal-bald
  ☆18Updated last year
• suinleelab / attributionpriors
  Tools for training explainable models using attribution priors.
  ☆124Updated 4 years ago
• Piyushi-0 / ACE
  Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.
  ☆51Updated 3 years ago
• adrianjav / heterogeneous_vaes
  Pytorch implementation of VAEs for heterogeneous likelihoods.
  ☆43Updated 3 years ago
• karalets / TyXe
  Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)
  ☆42Updated 4 years ago
• charmlab / recourse
  Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
  ☆37Updated 2 years ago
• namkoong-lab / robustopt
  ☆43Updated 7 years ago
• sjblim / rmsn_nips_2018
  ☆40Updated 6 years ago
• mrojascarulla / causal_transfer_learning
  ☆32Updated 7 years ago
• GoudetOlivier / CGNN
  Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"
  ☆100Updated 6 years ago
• d909b / cxplain
  Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
  ☆132Updated 5 years ago
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• msesia / deepknockoffs
  Approximate knockoffs and model-free variable selection.
  ☆56Updated 4 years ago
• ssydasheng / FBNN
  Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
  ☆85Updated 5 years ago
• gpleiss / equalized_odds_and_calibration
  Code and data for the experiments in "On Fairness and Calibration"
  ☆51Updated 3 years ago
• larslorch / avici
  Amortized Inference for Causal Structure Learning, NeurIPS 2022
  ☆68Updated 9 months ago
• d909b / ame
  🤖🤖 Attentive Mixtures of Experts (AMEs) are neural network models that learn to output both accurate predictions and estimates of featu…
  ☆42Updated 2 years ago
• anndvision / quince
  Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
  ☆23Updated 2 years ago
• bradyneal / realcause
  Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed caus…
  ☆80Updated 4 years ago
• debmandal / causality-fairness
  References for Papers at the Intersection of Causality and Fairness
  ☆18Updated 6 years ago