Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis

TL;DR

This paper introduces an open-source benchmarking framework for evaluating causal inference algorithms, using real-world covariates and simulated treatment effects to address validation challenges in healthcare data analysis.

Contribution

It provides a comprehensive, scalable, and open-source framework with novel metrics for assessing causal inference methods on realistic data.

Findings

Framework enables automatic evaluation of causal algorithms

Uses real-world covariates with simulated outcomes for validation

Addresses scaling and data-censoring challenges in causal analysis

Abstract

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