Alternatives and similar repositories for mliv

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

• rik-helwegen / CEVAE_pytorch
  ☆45Updated 6 years ago
• matthewvowels1 / Awesome-Causal-Inference
  A curated list of awesome work on causal inference, particularly in machine learning.
  ☆105Updated 4 years ago
• jsyoon0823 / GANITE
  Codebase for GANITE: Estimation of Individualized Treatment Effects using GANs - ICLR 2018
  ☆61Updated 5 years ago
• d909b / perfect_match
  ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.
  ☆128Updated 2 years ago
• WeijiaZhang24 / TEDVAE
  Code for TEDVAE, a VAE-based treatment effect estimation algorithm.
  ☆25Updated 2 years ago
• Osier-Yi / SITE
  ☆58Updated 3 years ago
• bradyneal / realcause
  Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed caus…
  ☆75Updated 4 years ago
• lushleaf / varying-coefficient-net-with-functional-tr
  [ICLR 2021] Code for: Varying Coefficient Neural Network with Functional Targeted Regularization for Estimating Continuous Treatment Effe…
  ☆77Updated 2 years ago
• hlzhang109 / TransTEE
  ☆33Updated 2 years ago
• paras2612 / CauseBox
  Causal inference is a critical task in various fields such as healthcare,economics, marketing and education. Recently, there have beensig…
  ☆21Updated 3 years ago
• ioanabica / SCIGAN
  Code for NeurIPS 2020 paper: "Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks" by I. Bica…
  ☆30Updated 4 years ago
• anpwu / Causal4Application
  ☆23Updated 3 years ago
• rguo12 / network-deconfounder-wsdm20
  Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.
  ☆75Updated 3 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
  ☆83Updated 7 years ago
• CausalML / DeepGMM
  ☆35Updated 5 years ago
• jhartford / DeepIV
  Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction
  ☆156Updated 3 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
• weirayao / leap
  LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network.
  ☆35Updated 2 years ago
• AliciaCurth / CATENets
  Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
  ☆140Updated 11 months ago
• ignavierng / notears-tensorflow
  Reimplementation of NOTEARS in Tensorflow
  ☆33Updated 2 years ago
• JeanKaddour / SIN
  Causal Effect Inference for Structured Treatments (SIN) (NeurIPS 2021)
  ☆42Updated 3 years ago
• cmu-phil / example-causal-datasets
  Example causal datasets with consistent formatting and ground truth
  ☆83Updated last month
• vdorie / npci
  Non-parametrics for Causal Inference
  ☆47Updated 3 years ago
• vveitch / causal-network-embeddings
  Software and pre-processed data for "Using Embeddings to Correct for Unobserved Confounding in Networks"
  ☆56Updated 2 years ago
• claudiashi57 / dragonnet
  ☆268Updated 3 years ago
• d909b / drnet
  💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatm…
  ☆88Updated 2 years ago
• ignavierng / golem
  On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
  ☆33Updated 3 years ago
• jmoss20 / notears
  Implementation of "DAGs with NO TEARS: Smooth Optimization for Structure Learning", by Zheng et al. (2018)
  ☆49Updated 6 years ago
• yixinwang / representation-causal-public
  ☆44Updated 3 years ago
• Biwei-Huang / Causal-Discovery-from-Nonstationary-Heterogeneous-Data
  Causal Discovery from Nonstationary/Heterogeneous Data.
  ☆53Updated 4 years ago