Alternatives and similar repositories for Interpretable-Embeddings-MNL

Users that are interested in Interpretable-Embeddings-MNL are comparing it to the libraries listed below

• huiyang-yi / Awesome-Causality-and-ML-Papers
  关于causal discovery, invariant learning, machine learning等方向的论文阅读笔记和slides总结
  ☆34Updated 6 months ago
• woshiyyya / ERPP-RMTPP
  A pytorch implementation of ERPP and RMTPP on ATM maintenance dataset.
  ☆55Updated 6 years ago
• ckassaad / causal_discovery_for_time_series
  Causal discovery for time series
  ☆103Updated 3 years ago
• iancovert / Neural-GC
  Granger causality discovery for neural networks.
  ☆233Updated 4 years ago
• kevinsbello / dagma
  A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matr…
  ☆137Updated last year
• geopanag / pandemic_tgnn
  ☆49Updated last year
• yangjm67 / TransPAI
  Transportation data online prediction
  ☆49Updated 4 years ago
• lmz123321 / proxy_causal_discovery
  Official implementation for NeurIPS23 paper: Causal Discovery from Subsampled Time Series with Proxy Variable
  ☆36Updated last year
• IntelLabs / causality-lab
  Causal discovery algorithms and tools for implementing new ones
  ☆242Updated 5 months ago
• kochbj / Deep-Learning-for-Causal-Inference
  Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflo…
  ☆340Updated last year
• CausalAILab / NeuralCausalModels
  Neural Causal Model (NCM) implementation by the authors of The Causal Neural Connection.
  ☆31Updated 3 years ago
• Hongrui24 / RMTPP-pytorch
  ☆18Updated 5 years ago
• Graph-Machine-Learning-Group / grin
  Official repository for the paper "Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks" (ICLR 2022)
  ☆174Updated 3 years ago
• sakhanna / SRU_for_GCI
  Statistical Recurrent Unit based time series generative models for detecting nonlinear Granger causality
  ☆35Updated 5 years ago
• NetworkDismantling / review
  Repository of scripts and data for the "Robustness and resilience of complex networks" paper by Oriol Artime, Marco Grassia, Manlio De Do…
  ☆92Updated 3 months ago
• xiangyu-sun-789 / NTS-NOTEARS
  Code for paper: NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge
  ☆27Updated 2 years ago
• RiccardoSpolaor / Verbal-Explanations-of-Spatio-Temporal-Graph-Neural-Networks-for-Traffic-Forecasting
  An eXplainable AI system to elucidate short-term speed forecasts in traffic networks obtained by Spatio-Temporal Graph Neural Networks.
  ☆24Updated last year
• Taehooie / CGChoiceModel
  Computational graph-based discrete choice models
  ☆13Updated last month
• AliciaCurth / CATENets
  Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
  ☆153Updated last year
• jmoss20 / notears
  Implementation of "DAGs with NO TEARS: Smooth Optimization for Structure Learning", by Zheng et al. (2018)
  ☆50Updated 6 years ago
• cmu-phil / example-causal-datasets
  Example causal datasets with consistent formatting and ground truth
  ☆101Updated 8 months ago
• Valentyn1997 / CausalTransformer
  Code for the paper "Causal Transformer for Estimating Counterfactual Outcomes"
  ☆174Updated last year
• abiodun-ayodeji / Structure-Learning-from-Time-series-Data-with-CausalNex
  Time series data structure learning with NOTEARS and DYNOTEARS
  ☆15Updated last year
• WeizhuQIAN / DeepSTUQ_Pytorch
  ☆11Updated last year
• jarrycyx / UNN
  Causal Neural Nerwork
  ☆144Updated 3 months ago
• chaoshangcs / GTS
  Discrete Graph Structure Learning for Forecasting Multiple Time Series, ICLR 2021.
  ☆179Updated 4 years ago
• deepkashiwa20 / MepoGNN
  [ECMLPKDD22] MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks
  ☆30Updated 2 years ago
• Ariza-Sun / MetroFlow
  MetroFlow is an open-sourced, city-scale metro flow dataset, covering the period of May-August 2017 and 302 metro stations in Shanghai, C…
  ☆25Updated 6 months ago
• EternityZY / STNSCM
  This is the official release code of AAAI2023 accepted paper: "Spatial temporal Neural Structural Causal Models for Bike Flow Prediction"
  ☆41Updated 2 years ago
• HCPLab-SYSU / HIAM
  ☆16Updated 2 years ago