Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis

causal inference

A framework for benchmarking causal inference models used in ACIC 2018 Data Challenge.


Yishai Shimoni

IBM Research

Chen Yanover

IBM Research

Ehud Karavani

IBM Research

Yaara Goldschmnidt

IBM Research


February 14, 2018


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 this URL.


  title={Benchmarking framework for performance-evaluation of causal inference analysis},
  author={Shimoni, Yishai and Yanover, Chen and Karavani, Ehud and Goldschmnidt, Yaara},
  journal={arXiv preprint arXiv:1802.05046},