An evaluation toolkit to guide model selection and cohort definition in causal inference

causal inference

A machine learning approach for evaluating causal inference models.


Yishai Shimoni

IBM Research

Ehud Karavani

IBM Research

Sivan Ravid

IBM Research

Peter Bak

IBM Research

Tan Hung Ng

IBM Watson Health

Sharon Hensley Alford

IBM Watson Health

Yaara Goldschmidt

IBM Research


June 2, 2019


Real world observational data, together with causal inference, allow the estimation of causal effects when randomized controlled trials are not available. To be accepted into practice, such predictive models must be validated for the dataset at hand, and thus require a comprehensive evaluation toolkit, as introduced here. Since effect estimation cannot be evaluated directly, we turn to evaluating the various observable properties of causal inference, namely the observed outcome and treatment assignment. We developed a toolkit that expands established machine learning evaluation methods and adds several causal-specific ones. Evaluations can be applied in cross-validation, in a train-test scheme, or on the training data. Multiple causal inference methods are implemented within the toolkit in a way that allows modular use of the underlying machine learning models. Thus, the toolkit is agnostic to the machine learning model that is used. We showcase our approach using a rheumatoid arthritis cohort (consisting of about 120K patients) extracted from the IBM MarketScan(R) Research Database. We introduce an iterative pipeline of data definition, model definition, and model evaluation. Using this pipeline, we demonstrate how each of the evaluation components helps drive model selection and refinement of data extraction criteria in a way that provides more reproducible results and ensures that the causal question is answerable with available data. Furthermore, we show how the evaluation toolkit can be used to ensure that performance is maintained when applied to subsets of the data, thus allowing exploration of questions that move towards personalized medicine.

This manuscript combines the evaluation suite with a specific use-case. A readers-digest version focusing on ROC curves for propensity score models is also available here


  title={An evaluation toolkit to guide model selection and cohort definition in causal inference},
  author={Shimoni, Yishai and Karavani, Ehud and Ravid, Sivan and Bak, Peter and Ng, Tan Hung and Alford, Sharon Hensley and Meade, Denise and Goldschmidt, Yaara},
  journal={arXiv preprint arXiv:1906.00442},