Decisions drive vision, strategy, execution, evaluation, problem-solving, performance, and continuous improvement.1 A decision is an irrevocable commitment of time and resources. Every decision has an opportunity cost—the loss benefit of the better option not chosen or not considered (see SDG.com).
Decision competent managers use and leverage data, information, and knowledge management systems to improve decision quality, problem-solving, and performance improvement. Driven by decision makers’ priorities and questions, data scientists transform data into information and actionable knowledge to inform, influence, or optimize decision making;2 and implementation scientists translate evidence into practice.
Introduction At the San Francisco Department of Public Health, Population Health Division, we promote Decision Quality (DQ) [1,2]. DQ starts by knowing what a good decision looks like. A good decision is built with six quality requirements (Table 1).
Table 1: Decision quality requirements: A decision is only as strong as its weakest link Name Quality requirements Key question Frame Appropriate frame What are we deciding and why?
In Part 1 we introduced directed acyclic graphs (DAGs)  as a better way to represent program theory . A DAG is a Bayesian network where each directed arrow represents a causal link, not merely a probabilistic dependency .
Program theory has three components:
Theory of causation (causal model) Theory of change (selected strategy) Theory of action (selected intervention that activates change) Program theory is often called “theory of change.
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