IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework

Causality-Benchmark is a library developed by IBM Research Haifa for benchmarking algorithms that estimate the causal effect of a treatment on some outcome. The framework includes unlabeled data, labeled data, and code for scoring algorithm predictions. Currently, the framework contains one essential dataset, a feature matrix that is derived from the linked birth and infant death data, and the labeled and unlabeled data are simulated models of the treatment assignment, treatment effect and censoring data based on it. More details regarding the data can be found in the LBIDD README file. However, the evaluation script is not bounded to the provided data, and can be used on other data as long as some basic requirements are kept regarding the formats.