Skip to main content

Extensions & abstractions of advanced econometric techniques leveraging machine learning.

Project description

CaML Logo

image PyPI - Downloads lifecycle pre-commit ruff uv
Caml CI/CD Build & Publish Docs Pre-Commit & Linting Checks
Codacy Badge Codacy Badge

Causal Machine Learning

Welcome!

CaML provides a high-level API for an opinionated framework in performing Causal ML to estimate Average Treatment Effects (ATEs), Group Average Treatment Effects (GATEs), and Conditional Average Treatment Effects (CATEs), and to provide mechanisms to utilize these models for out of sample validation, prediction, & policy prescription.

The codebase is comprised primarily of extensions & abstractions over top of EconML & DoubleML with techniques motivated heavily by Causal ML Book and additional research.

Background

The origins of CaML are rooted in a desire to develop a set of helper tools to abstract and streamline techniques & best pratices in Causal ML/Econometrics for estimating ATEs, GATEs, and CATEs, along with policy prescription. In addition, we seek to provide a framework for validating & scoring these models on out of sample data to help set the foundations for an AutoML framework for CATE models.

As we began working on these helper tools, we begun to see the value in reformulating this framework into a reusable package for wider use amongst the community and to provide an opinionated framework that can be integrated into productionalized systems, particularly experimentation platforms, for efficient estimation of causal parameters for reporting & decision-making purposes.

All of the standard assumptions for causal inference still apply in order for these tools & techniques to provide unbiased inference. A great resource for the CausalML landscape is the CausalML book written and publicly available generously by V. Chernozhukov, C. Hansen, N. Kallus, M. Spindler, & V. Syrgkanis.

Given a key motivation is to provide a tool for productionalized systems, we are building this package with interoperability and extensibility as core values. As of now, the tools utilized still rely on in-memory datasets for estimation (via EconML for causal models & flaml for AutoML of nuissance functions), but we leverage Ray & Spark for distributing certain processes where appropriate and if available for the user.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

caml-0.0.0.dev15.tar.gz (279.0 kB view details)

Uploaded Source

Built Distribution

caml-0.0.0.dev15-py3-none-any.whl (38.6 kB view details)

Uploaded Python 3

File details

Details for the file caml-0.0.0.dev15.tar.gz.

File metadata

  • Download URL: caml-0.0.0.dev15.tar.gz
  • Upload date:
  • Size: 279.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for caml-0.0.0.dev15.tar.gz
Algorithm Hash digest
SHA256 77188a72b4c94a822ac9d99c487337b6654fc04c6b20b14c1fe18e41c0eb80b2
MD5 6b5c3ecb41fe42819b1d98976024e653
BLAKE2b-256 75030032e003644b8b38014fd18bb8f9853e21e989e5733729f2959090ab9912

See more details on using hashes here.

File details

Details for the file caml-0.0.0.dev15-py3-none-any.whl.

File metadata

  • Download URL: caml-0.0.0.dev15-py3-none-any.whl
  • Upload date:
  • Size: 38.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for caml-0.0.0.dev15-py3-none-any.whl
Algorithm Hash digest
SHA256 c55f4984a81a993854b38c4d60244bc0ccabc86ee244265f877e8f19aa7a4249
MD5 80b268394f0867601f108cb3f2c0244c
BLAKE2b-256 8791d625a616b02d6ed7556733b240595ca3e5338c81363ad47f948596545638

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page