causalkit: A Rust Package for Causal Inference

causalkit is a rust package that implements a set of algorithms for causal inference modeling. Currently, it supports two tree-based algorithms for individual treatment effect estimation with binary response and continuous response respectively. Given an intervention T and response Y, it estimates the individual treatment effect (ITE) for each sample. Both algorithms support multiple treatments, e.g. for an intervention with K treatments (control, treatment_1, treatment_2, ..., treatment_K), it will output the uplift in treatment effect for each treatment_i against control.

• Tree-based algorithms
  • Binary Response: Uplift Random Forest on KL-Divergence [1]
  • Continuous Response: Uplift Random Forest with honest estimation [2]

This modeling approach can be applied to problems where the personalized effectiveness of an intervention T to target users is concerned.

Installation

It requires python version >=3.8. To use it in python, install as follows

pip install causalkit

You can also download the source code from this github repository and build the python library by yourself via maturin.

Usage

Please check the jupyter notebook in the example folder to learn how to use the library.

References

Literature

1. Stefan Wager, Susan Athey. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests" arXiv preprint arXiv:1510.04342 (2015).
2. Susan Athey, Guido Imbens. "Recursive Partitioning for Heterogeneous Causal Effects" arXiv preprint arXiv:1504.01132 (2015).