Alternatives and similar repositories for cfqp:

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

• facebookresearch / CausalRepID
  Experiments to reproduce results in Interventional Causal Representation Learning.
  ☆25Updated 2 years ago
• patrickrchao / DiffusionBasedCausalModels
  ☆24Updated last year
• weirayao / leap
  LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network.
  ☆35Updated 2 years ago
• JeanKaddour / SIN
  Causal Effect Inference for Structured Treatments (SIN) (NeurIPS 2021)
  ☆42Updated 2 years ago
• 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
• ahujak / IB-IRM
  ☆30Updated 3 years ago
• seedatnabeel / TE-CDE
  Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)
  ☆27Updated 2 years ago
• mrojascarulla / causal_transfer_learning
  ☆32Updated 6 years ago
• xiangwang1223 / reinforced_causal_explainer
  Reinforced Causal Explainer for Graph Neural Networks, TPAMI2022
  ☆33Updated 2 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
• yixinwang / representation-causal-public
  ☆43Updated 3 years ago
• tapilab / emnlp-2020-spurious
  ☆17Updated 3 years ago
• AMLab-Amsterdam / DataAugmentationInterventions
  ☆27Updated 4 years ago
• kakaobrain / irm-empirical-study
  An Empirical Study of Invariant Risk Minimization
  ☆27Updated 4 years ago
• siplab-gt / generative-causal-explanations
  Code for "Generative causal explanations of black-box classifiers"
  ☆33Updated 4 years ago
• inspire-group / robustness-via-transport
  ☆12Updated 5 years ago
• wangwllu / provably_robust_metric_learning
  A metric learning method to learn a provably robust Mahalanobis distance
  ☆10Updated 3 years ago
• mertyg / beyond-confidence-atypicality
  Repository for the NeurIPS 2023 paper "Beyond Confidence: Reliable Models Should Also Consider Atypicality"
  ☆12Updated 10 months ago
• StephLong614 / Causal-disco-LLM-imperfect-experts
  ☆13Updated 8 months ago
• mostafaalishahi / eICU_Benchmark_updated
  An updated version of eICU Benchmark with an updated problem definition on LoS and Decompensation tasks
  ☆11Updated 3 years ago
• ahxt / G2R
  [WWW2022] Geometric Graph Representation Learning via Maximizing Rate Reduction
  ☆26Updated 2 years ago
• CausalAILab / NCMCounterfactuals
  ☆12Updated last year
• hongyuanmei / nce-mpp
  Source code for Noise-Contrastive Estimation for Multivariate Point Processes (NeurIPS 2020).
  ☆15Updated 4 years ago
• jiaqima / CopulaGNN
  CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks (ICLR 2021)
  ☆13Updated 2 years ago
• lmz123321 / which_invariance
  Official implementation for ICML23 paper: Which Invariance Should We Transfer? A Causal Minimax Learning Approach
  ☆11Updated 8 months ago
• vios-s / DiffAN
  Diffusion Models for Causal Discovery
  ☆85Updated last year
• wanyu-lin / ICML2021-Gem
  Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."
  ☆65Updated 2 years ago
• charmlab / recourse
  Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
  ☆36Updated 2 years ago
• DeepGraphLearning / SPN
  ☆29Updated 2 years ago
• ermongroup / BCD-Nets
  Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021
  ☆24Updated 3 years ago