
# 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}
}
```
{"license": "mit"}
arxiv:2205.12934 license:mit region:us
TAGS
#arxiv-2205.12934 #license-mit #region-us 


# Pretrained 'avici' models

The neural networks trained in amortized variational inference for 
causal discovery (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, 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 'URL' file in the respective
model folder.


- License: MIT
- Repository: URL
- Paper: "Amortized Inference for Causal Structure Learning"
(Lorch et al., 2022, NeurIPS 2022).
- Demo: 
![Open In Colab](URL




## Reference


# Pretrained 'avici' models

The neural networks trained in amortized variational inference for 
causal discovery (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, 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 'URL' file in the respective
model folder.


- License: MIT
- Repository: URL
- Paper: "Amortized Inference for Causal Structure Learning"
(Lorch et al., 2022, NeurIPS 2022).
- Demo: 
![Open In Colab](URL ## Reference
TAGS
#arxiv-2205.12934 #license-mit #region-us 
 # Pretrained 'avici' models

The neural networks trained in amortized variational inference for 
causal discovery (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, 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 'URL' file in the respective
model folder.


- License: MIT
- Repository: URL
- Paper: "Amortized Inference for Causal Structure Learning"
(Lorch et al., 2022, NeurIPS 2022).
- Demo: 
![Open In Colab](URL ## Reference
TAGS
#arxiv-2205.12934 #license-mit #region-us 
# Pretrained 'avici' models

The neural networks trained in amortized variational inference for 
causal discovery (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, 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 'URL' file in the respective
model folder.


- License: MIT
- Repository: URL
- Paper: "Amortized Inference for Causal Structure Learning"
(Lorch et al., 2022, NeurIPS 2022).
- Demo: 
![Open In Colab](URL## Reference