A practical guide to causal inference in healthcare operations using single-world intervention graphs (SWIGs)

Observational studies in operations management (OM) increasingly guide managerial, clinical, and policy decisions in healthcare. To strengthen their rigor, empirical OM research has turned toward causal inference. However, reliably attributing specific effects to interventions using observational data remains challenging. This tutorial describes a causal inference approach for healthcare OM, centered on single-world intervention graphs, which unify the potential outcomes and do-calculus frameworks. We emphasize constructing precise causal questions and determining when causal effects can be identified from observed variables. We present a detailed case study examining whether a longer treatment time in the emergency department can reduce unnecessary admissions without adversely affecting downstream patient outcomes. The example shows what applying the proposed framework looks like in practice, from defining causal questions and encoding assumptions graphically to walking through the identification process, thereby illustrating how structured causal reasoning can inform healthcare OM.

• 저널Production and Operations Management
• 토픽Advanced Causal Inference Techniques · Statistics and Probability
• DOI10.1177/10591478261483354
• 저자Amy L. Cochran, Sebastian Alejandro Alvarez-Avendaño, Fernando A. Acosta-Perez, Keith E. Kocher, Brian William Patterson, Gabriel Zayas‐Cabán