readme: ---
  license: mit
  ---
  
  # Pretrained `avici` models
  
  The neural networks trained in **a**mortized **v**ariational **i**nference for 
  **c**ausal d**i**scovery (AVICI) **infer causal structure 
  from data** based on a simulator of the domain of interest.
  By being trained on simulated data, the models can acquire 
  realistic inductive biases from prior knowledge
  that is hard to cast as score functions or conditional 
  independence tests used in classical causal inference.
  
  This is a collective model card for the original and follow-up
  checkpoints of the paper
  ([Lorch et al., 2022](https://arxiv.org/abs/2205.12934), NeurIPS 2022).
  
  - `scm-v0`
  - `neurips-linear`
  - `neurips-rff`
  - `neurips-grn`
  
  All models share the same architecture and training
  parameters and only differ in their synthetic training 
  data distributions.
  The sampling distributions
  are specified in the `config.yaml` file in the respective
  model folder.
  
  
  - **License:** MIT
  - **Repository:** [https://github.com/larslorch/avici](https://github.com/larslorch/avici)
  - **Paper:** "Amortized Inference for Causal Structure Learning"
  ([Lorch et al., 2022](https://arxiv.org/abs/2205.12934), NeurIPS 2022).
  - **Demo:** 
  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/larslorch/avici/blob/master/example-pretrained/example.ipynb)
  
  
  
  
  ## Reference
  
  ```
  @article{lorch2022amortized,
    title={Amortized Inference for Causal Structure Learning},
    author={Lorch, Lars and Sussex, Scott and Rothfuss, Jonas and Krause, Andreas and Sch{\"o}lkopf, Bernhard},
    journal={Advances in Neural Information Processing Systems},
    volume={35},
    year={2022}
  }
  ```