Alternatives and similar repositories for iap-cidl

Users that are interested in iap-cidl are comparing it to the libraries listed below

• nke001 / causal_learning_unknown_interventions
  Code for "Neural causal learning from unknown interventions"
  ☆104Updated 5 years ago
• diviyank / SAM
  Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)
  ☆54Updated 4 years ago
• blei-lab / deconfounder_tutorial
  ☆87Updated 5 years ago
• OATML / ucate
  Uncertainty in Conditional Average Treatment Effect Estimation
  ☆33Updated 4 years ago
• GoudetOlivier / CGNN
  Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"
  ☆100Updated 6 years ago
• clinicalml / gumbel-max-scm
  Code for "Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models" (ICML 2019)
  ☆45Updated 5 years ago
• d909b / cxplain
  Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
  ☆132Updated 5 years ago
• jhartford / DeepIV
  Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction
  ☆160Updated 4 years ago
• mkocaoglu / CausalGAN
  ☆146Updated 8 years ago
• logangraham / arXausality
  A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.
  ☆415Updated 5 years ago
• AMLab-Amsterdam / CEVAE
  Causal Effect Inference with Deep Latent-Variable Models
  ☆354Updated 5 years ago
• Jianbo-Lab / L2X
  ☆125Updated 4 years ago
• gpleiss / equalized_odds_and_calibration
  Code and data for the experiments in "On Fairness and Calibration"
  ☆51Updated 3 years ago
• oxcsml / ML_bazaar
  OxCSML research group reading groups and meetings at the Department of Statistics, University of Oxford.
  ☆94Updated 4 years ago
• authors-1901-10912 / A-Meta-Transfer-Objective-For-Learning-To-Disentangle-Causal-Mechanisms
  Code for "A Meta Transfer Objective For Learning To Disentangle Causal Mechanisms"
  ☆127Updated 6 years ago
• microsoft / EDDI
  Code for paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
  ☆42Updated 2 years ago
• mrojascarulla / causal_transfer_learning
  ☆32Updated 7 years ago
• debmandal / RL-Causality
  References at the Intersection of Causality and Reinforcement Learning
  ☆89Updated 5 years ago
• jsyoon0823 / INVASE
  Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR
  ☆64Updated 5 years ago
• charmlab / recourse
  Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
  ☆37Updated 2 years ago
• lightlightdyy / Deep-Learning-and-Causal-Inference
  ☆30Updated 7 years ago
• ignavierng / notears-tensorflow
  Reimplementation of NOTEARS in Tensorflow
  ☆33Updated 2 years ago
• d909b / ame
  🤖🤖 Attentive Mixtures of Experts (AMEs) are neural network models that learn to output both accurate predictions and estimates of featu…
  ☆43Updated 2 years ago
• clinicalml / dmm
  Deep Markov Models
  ☆134Updated 6 years ago
• d909b / perfect_match
  ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.
  ☆131Updated 2 years ago
• lopezpaz / causation_learning_theory
  Code for "Towards a learning theory of cause-effect inference" (ICML 2015).
  ☆30Updated 5 years ago
• Piyushi-0 / ACE
  Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.
  ☆51Updated 4 years ago
• PGM-Lab / InferPy
  InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy
  ☆147Updated last year
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆101Updated 7 years ago
• IBM-HRL-MLHLS / IBM-Causal-Inference-Benchmarking-Framework
  Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring code
  ☆84Updated 7 years ago