Alternatives and similar repositories for BCD-Nets

Users that are interested in BCD-Nets are comparing it to the libraries listed below

• seedatnabeel / TE-CDE
  Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)
  ☆26Updated 3 years ago
• kurowasan / GraN-DAG
  ☆97Updated 2 years ago
• xwshen51 / DEAR
  Disentangled gEnerative cAusal Representation (DEAR)
  ☆65Updated 3 years ago
• xiaoshuai09 / Wasserstein-Learning-For-Point-Process
  learning point processes by means of optimal transport and wasserstein distance
  ☆54Updated 7 years ago
• ilkhem / iVAE
  VAEs and nonlinear ICA: a unifying framework
  ☆40Updated 5 years ago
• sjblim / rmsn_nips_2018
  ☆40Updated 7 years ago
• HongtengXu / s-gwl
  Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
  ☆44Updated 6 years ago
• ioanabica / Counterfactual-Recurrent-Network
  Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…
  ☆67Updated last year
• yannadani / vcn_pytorch
  Official Implementation of the paper "Variational Causal Networks: Approximate Bayesian Inference over Causal Structures"
  ☆17Updated 4 years ago
• mrojascarulla / causal_transfer_learning
  ☆32Updated 7 years ago
• weirayao / leap
  LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network.
  ☆40Updated 3 years ago
• piomonti / carefl
  Code for "Causal autoregressive flows" - AISTATS, 2021
  ☆45Updated 4 years ago
• slachapelle / dcdi
  Repository for "Differentiable Causal Discovery from Interventional Data"
  ☆77Updated 4 years ago
• sigvae / SIGraphVAE
  ☆26Updated 6 years ago
• BorisMuzellec / MissingDataOT
  A Pytorch implementation of missing data imputation using optimal transport.
  ☆105Updated 4 years ago
• siamakz / iVAE
  VAEs and nonlinear ICA: a unifying framework
  ☆50Updated 6 years ago
• ermongroup / graphite
  Code for Graphite iterative graph generation
  ☆59Updated 6 years ago
• ioanabica / Time-Series-Deconfounder
  Code for ICML 2020 paper: "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders" by I. …
  ☆52Updated 5 years ago
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆35Updated 5 years ago
• fishmoon1234 / DAG-NoCurl
  ☆25Updated 4 years ago
• wanyu-lin / ICML2021-Gem
  Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."
  ☆66Updated 3 years ago
• rik-helwegen / CEVAE_pytorch
  ☆45Updated 6 years ago
• matenure / OTCoarsening
  Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
  ☆37Updated 4 years ago
• psanch21 / VACA
  ☆24Updated 3 years ago
• vios-s / DiffAN
  Diffusion Models for Causal Discovery
  ☆90Updated 2 years ago
• pamattei / miwae
  Implementation of the MIWAE method for deep generative modelling of incomplete data sets.
  ☆41Updated last year
• HongtengXu / gwl
  Gromov-Wasserstein Learning for Graph Matching and Node Embedding
  ☆73Updated 6 years ago
• tvayer / FGW
  Code for Optimal Transport for structured data with application on graphs
  ☆104Updated 3 years ago
• vislearn / GIN
  Code for the paper "Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)" (2020)
  ☆33Updated 4 years ago
• siplab-gt / generative-causal-explanations
  Code for "Generative causal explanations of black-box classifiers"
  ☆35Updated 5 years ago