Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis (1802.05046v2)

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

• The paper introduces a comprehensive benchmarking framework to evaluate causal inference algorithms, addressing the lack of ground truth by using simulated data based on real-world covariates.
• This framework employs multiple non-redundant scoring metrics such as ENoRMSE and bias, alongside simulated datasets incorporating realistic covariates, to offer nuanced insights into algorithm performance.
• The openly accessible framework standardizes the comparison and validation of causal inference methods, promoting advancements and reliable adoption in fields like healthcare by providing a robust evaluation toolkit.

Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis

The paper "Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis" addresses the complex challenge of evaluating causal inference methods, particularly in the field of healthcare data. Causal inference is pivotal in estimating the effects of treatments, allowing researchers to draw conclusions about potential outcomes from interventions that have not been observed. However, unlike traditional machine learning tasks, causal inference lacks direct ground truth for counter-factual scenarios, which makes evaluation inherently difficult.

The authors propose a comprehensive benchmarking framework specifically designed for assessing causal inference algorithms. This framework bridges the gap by providing tools to evaluate algorithm predictions through a structured dataset and evaluation code. The dataset comprises both unlabeled and labeled data, where the former is utilized for prediction and the latter for validation. The framework's foundation is built upon simulations based on real-world covariates, thus creating a pseudo-ground truth for benchmarking.

Key Features of the Framework

1. Data Composition: The dataset integrates real-world covariate data derived from the publicly available Linked Births and Infant Deaths Database (LBIDD), establishing a realistic baseline for simulations. It includes:
   • Covariate tables representing clinical measurements.
   • Observation files simulating treatment assignments and observed outcomes.
   • Counter-factual files providing the simulated outcomes for both treated and untreated scenarios, serving as the labeled data for validation.
2. Evaluation Metrics: The framework introduces multiple non-redundant scoring metrics, critical for a multi-faceted evaluation. These include effect-normalized root mean squared error (ENoRMSE), RMSE, bias, coverage of confidence intervals, confidence interval credibility (CIC), and effect normalized confidence interval size (ENCIS). Each metric provides unique insights into algorithm performance, particularly in understanding both the accuracy and precision of causal estimations.
3. Simulated Data Generation: To address the unavailability of true counter-factual data, the authors employ a simulation-based approach. This involves crafting a causal graph that determines treatment assignments and outcomes based on covariates, ensuring no unmeasured confounders impact the results. The simulation parameters are defined to include varying degrees of non-linearity, treatment prevalence, and noise, providing robust scenarios for testing.
4. Addressing Scaling and Censoring: Two primary challenges in causal inference—scaling with data size and handling data censoring—are explicitly tackled. Separate datasets are provided to test methods across different data sizes and to evaluate performance under conditions where certain outcomes are censored.

Implications and Future Directions

Practically, this framework enables a standardized method to compare causal inference algorithms, fostering advancement in identifying effective and reliable methods for real-world applications. Theoretically, it sets the stage for deeper exploration into the relationship between specific data characteristics and algorithm performance, which is crucial for method selection in diverse scenarios.

In future developments, the community's contributions, such as new datasets and metrics, can be integrated into this open-source framework, extending its utility and relevance. As causal inference continues to evolve, such frameworks will be indispensable in ensuring replicability and validity of research findings across multiple domains, particularly in healthcare, where treatment decisions can be life-changing.

This paper's contribution lies in providing a robust, openly accessible toolkit for researchers, which potentially accelerates the validation and adoption of causal inference methodologies, crucial for informed decision-making in data-driven fields.