Alternatives and similar repositories for deconfounder_tutorial:

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

• blei-lab / deconfounder_public
  ☆20Updated 2 years ago
• maxwshen / iap-cidl
  Causal Inference & Deep Learning, MIT IAP 2018
  ☆85Updated 7 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
• AMLab-Amsterdam / CEVAE
  Causal Effect Inference with Deep Latent-Variable Models
  ☆333Updated 4 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
  ☆82Updated 6 years ago
• ijmbarr / notes-on-causal-inference
  Some notes on Causal Inference, with examples in python
  ☆152Updated 5 years ago
• bradyneal / realcause
  Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed caus…
  ☆72Updated 3 years ago
• d909b / perfect_match
  ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.
  ☆126Updated last year
• gpleiss / equalized_odds_and_calibration
  Code and data for the experiments in "On Fairness and Calibration"
  ☆50Updated 2 years ago
• diviyank / SAM
  Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)
  ☆52Updated 4 years ago
• adith387 / slates_semisynth_expts
  Semi-synthetic experiments to test several approaches for off-policy evaluation and optimization of slate recommenders.
  ☆43Updated 7 years ago
• debmandal / causality-fairness
  References for Papers at the Intersection of Causality and Fairness
  ☆18Updated 6 years ago
• usaito / counterfactual-cv
  (ICML2020) “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models’’
  ☆31Updated last year
• d909b / cxplain
  Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
  ☆130Updated 4 years ago
• nke001 / causal_learning_unknown_interventions
  Code for "Neural causal learning from unknown interventions"
  ☆99Updated 4 years ago
• bradyneal / causal-book-code
  ☆76Updated 4 years ago
• blei-lab / causal-text-embeddings
  Software and data for "Using Text Embeddings for Causal Inference"
  ☆124Updated 4 years ago
• GoudetOlivier / CGNN
  Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"
  ☆99Updated 5 years ago
• clinicalml / cfrnet
  Counterfactual Regression
  ☆304Updated 2 years ago
• finnhacks42 / causal_bandits
  ☆16Updated 8 years ago
• vub-dl / cs-um
  Causal Simulations for Uplift Modeling
  ☆12Updated 5 years ago
• HongtengXu / PoPPy
  A Point Process Toolbox Based on PyTorch
  ☆130Updated 4 years ago
• bd2kccd / py-causal
  ☆204Updated last year
• ignavierng / notears-tensorflow
  Reimplementation of NOTEARS in Tensorflow
  ☆35Updated last year
• AliciaCurth / CATENets
  Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
  ☆136Updated 8 months ago
• d909b / drnet
  💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatm…
  ☆87Updated last year
• CausalML / continuous-policy-learning
  ☆11Updated 5 years ago
• ssamot / causality
  Kaggle's Causality Challenge Solution for team FirfiD
  ☆26Updated 11 years ago
• ioanabica / Counterfactual-Recurrent-Network
  Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…
  ☆58Updated 11 months ago
• bradyneal / causal-inference-books
  Flowchart to help choose which causal inference book to read. See https://bradyneal.github.io/which-causal-inference-book for more info s…
  ☆58Updated 5 years ago