https://github.com/markean/aimz

Scalable probabilistic impact modeling
https://github.com/markean/aimz

bayesian-inference probabilistic-modeling uncertainty-quantification

Last synced: 8 months ago
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Scalable probabilistic impact modeling

• Host: GitHub
• URL: https://github.com/markean/aimz
• Owner: markean
• License: apache-2.0
• Created: 2025-06-26T14:13:57.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2025-08-30T01:18:35.000Z (10 months ago)
• Last Synced: 2025-08-30T15:34:11.621Z (10 months ago)
• Topics: bayesian-inference, probabilistic-modeling, uncertainty-quantification
• Language: Python
• Homepage: https://aimz.readthedocs.io
• Size: 1.64 MB
• Stars: 4
• Watchers: 1
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: .github/CODE_OF_CONDUCT.md
  • Codeowners: .github/CODEOWNERS

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README

          
# aimz: Scalable probabilistic impact modeling

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[![DOI](https://zenodo.org/badge/1009062911.svg)](https://doi.org/10.5281/zenodo.16101876)

[**Installation**](https://aimz.readthedocs.io/stable/getting_started/installation.html) |

[**Tutorial**](https://aimz.readthedocs.io/latest/getting_started/tutorial.html) |

[**User Guide**](https://aimz.readthedocs.io/latest/user_guide/index.html) |

[**FAQs**](https://aimz.readthedocs.io/latest/faq.html) |

[**Changelog**](https://aimz.readthedocs.io/latest/changelog.html)

## Overview

aimz is a Python library for scalable probabilistic impact modeling, enabling assessment of intervention effects on outcomes with a streamlined interface for fitting, sampling, prediction, and effect estimation—minimal boilerplate, accelerated execution, and powered by [NumPyro](https://num.pyro.ai/en/stable/), [JAX](https://jax.readthedocs.io/en/latest/), [Xarray](https://xarray.dev/), and [Zarr](https://zarr.readthedocs.io/en/stable/).

## Features

- Intuitive API combining the ease of use from ML frameworks with the flexibility of probabilistic modeling.

- Accelerated computation via parallelism and distributed data.

- Support for interventional causal inference for counterfactuals and causal effects.

- MLflow integration for experiment tracking and model management.

## Usage

```python

from aimz import ImpactModel

# Define probabilistic model (kernel) using Numpyro primitives

def model(X, y=None):

    ...

# Load or prepare data

X, y = ...

# Initialize ImpactModel

im = ImpactModel(

    model,

    rng_key=...,      # e.g., jax.random.key(0)

    inference=...,    # e.g., SVI (or MCMC)

)

# Fit model and draw posterior samples

im.fit(X, y)

# Make predictions or posterior predictive samples

dt = im.predict(X)

```

## Contributing

See the [Contributing Guide](https://aimz.readthedocs.io/latest/development/contributing.html) to get started.