CauseBox: A Causal Inference Toolbox for Benchmarking Treatment Effect Estimators with Machine Learning Methods

Causal inference is a critical task in various fields such as healthcare, economics, marketing and education. Recently, there have been significant advances through the application of machine learning techniques, especially deep neural networks. Unfortunately, to-date many of the proposed methods are evaluated on different (data, software/hardware, hyperparameter) setups and consequently it is nearly impossible to compare the efficacy of the available methods or reproduce results presented in original research manuscripts. In this paper, we propose a causal inference toolbox (CauseBox) that addresses the aforementioned problems. At the time of publication, the toolbox includes seven state of the art causal inference methods and two benchmark datasets. By providing convenient command-line and GUI-based interfaces, the CauseBox toolbox helps researchers fairly compare the state of the art methods in their chosen application context against benchmark datasets. The code is made public at github.com/paras2612/CauseBox.