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In solving real-world problems like changing healthcare-seeking behaviors,designing interventions to improve downstream outcomes requires anunderstanding of the causal links within the system. Causal Bayesian Networks(BN) have been proposed as one such powerful method. In real-worldapplications, however, confidence in the results of BNs are often moderate atbest. This is due in part to the inability to validate against some groundtruth, as the DAG is not available. This is especially problematic if thelearned DAG conflicts with pre-existing domain doctrine. At the policy level,one must justify insights generated by such analysis, preferably accompanyingthem with uncertainty estimation. Here we propose a causal extension to thedatasheet concept proposed by Gebru et al (2018) to include approximate BNperformance expectations for any given dataset. To generate the results for aprototype Causal Datasheet, we constructed over 30,000 synthetic datasets withproperties mirroring characteristics of real data. We then recorded the resultsgiven by state-of-the-art structure learning algorithms. These results wereused to populate the Causal Datasheet, and recommendations were automaticallygenerated dependent on expected performance. As a proof of concept, we used ourCausal Datasheet Generation Tool (CDG-T) to assign expected performanceexpectations to a maternal health survey we conducted in Uttar Pradesh, India.

Causal datasheet: An approximate guide to practically assess Bayesian networks in the real world