  Causal inference analysis is the estimation of the effects of actions on
outcomes. In the context of healthcare data this means estimating the outcome
of counter-factual treatments (i.e. including treatments that were not
observed) on a patient's outcome. Compared to classic machine learning methods,
evaluation and validation of causal inference analysis is more challenging
because ground truth data of counter-factual outcome can never be obtained in
any real-world scenario. Here, we present a comprehensive framework for
benchmarking algorithms that estimate causal effect. The framework includes
unlabeled data for prediction, labeled data for validation, and code for
automatic evaluation of algorithm predictions using both established and novel
metrics. The data is based on real-world covariates, and the treatment
assignments and outcomes are based on simulations, which provides the basis for
validation. In this framework we address two questions: one of scaling, and the
other of data-censoring. The framework is available as open source code at
https://github.com/IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework
