caml 0.0.0.dev16

Extensions & abstractions of advanced econometric techniques leveraging machine learning.

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.