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Causal Datasheet for Datasets: An Evaluation Guide for Real-World Data Analysis and Data Collection Design Using

Bradley Butcher¹, Vincent S Huang², Christopher Robinson¹

¹Department of Informatics, Predictive Analytics Lab (PAL), University of Sussex, Brighton, United Kingdom.

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|August 2, 2021

Summary

This summary is machine-generated.

Causal Datasheets help global health researchers build confidence in Bayesian Networks (BNs) by estimating performance and sample size needs. This tool uses synthetic data to validate causal discovery methods, aiding policy and intervention design.

Keywords:

bayesian network big data causal modeling causality lower middle income country machine learning

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Area of Science:

Causal inference and machine learning
Global health research methodology
Data-driven policy and intervention design

Background:

Observational data in global health is abundant but often underutilized for causal discovery.
Causal Bayesian Networks (BNs) can represent causal relationships as Directed Acyclic Graphs (DAGs) from observational data.
Lack of confidence in BN results, due to inability to validate against ground truth, hinders adoption in global health.

Purpose of the Study:

To conceptualize and demonstrate a "Causal Datasheet" to approximate and document BN performance expectations.
To provide practitioners with confidence and sample size requirements for applying BNs to real-world datasets.
To aid analysis decisions in global health research by providing performance estimates for causal discovery.

Main Methods:

Developed a tool to generate synthetic Bayesian Networks and associated datasets mimicking real-world data.
Recorded results from structure learning algorithms and a novel OrderMCMC implementation using the Quotient Normalized Maximum Likelihood score.
Populated Causal Datasheets with performance estimates to inform recommendations based on user-defined thresholds.

Main Results:

Created Causal Datasheets to guide sample size determination for a sexual and reproductive health study in India.
Estimated the performance of BNs for a maternal health survey in India.
Validated generated performance estimates and investigated limitations using the ALARM dataset.

Conclusions:

Causal Datasheets enhance practitioner confidence in applying Bayesian Networks for causal discovery in global health.
The developed tool and methodology provide a framework for assessing BN performance on synthetic and real-world data.
This approach supports evidence-based decision-making for policy, program evaluation, and intervention design in low- and middle-income countries.