Constructing Causal Networks Through Regressions: A Tutorial

Significant progress has been made in the practice of conducting causal analysis using network models. Despite this progress, there is limited evidence that hospital risk managers are using these analytical models.

This article introduces the causal network, its related concepts, and methods of analysis. The article demonstrates how hospital risk managers can use existing regression software to construct a causal network and identify root causes of an adverse event.

Causal networks depict cause and effect in a set of variables. In this context, causes are strong correlations that meet 3 additional criteria: (1) causes occur prior to effects, (2) there is an articulated mechanism for how causes lead to effects, and (3) the association between cause and effect is not spurious, meaning the association persists even after other variables are statistically controlled for (a method of analysis called counterfactual). A causal network can be constructed through repeated use of least absolute shrinkage and selection operator (LASSO) regression. In the proposed regressions, the response variable is any variable in the data. The independent variables are variables that occur prior to the response variable. By design, the statistically significant coefficients in the time-constrained LASSO regressions identify "direct" causes of the response variables. When direct causes of all variables are identified, then the entire network model, including root causes, has been specified. In the final step, the parameters of the network model (ie, strength of causal associations) are estimated by fitting the network structure to the available data. We demonstrate these concepts through fitting a network model to simulated data for causes of excessive boarding in emergency departments.

The network (involving 12 causes, over 4 periods, and 1 sentinel event) was accurately recovered from the simulated case reports. The recovered network did not differ from the original network used to simulate the data in any of the 156 possible links. The recovered network allowed the identification of root and direct causes. It showed that hospital occupancy rate, and not emergency department efficiency, was root cause of excessive emergency department boarding.

Causal networks can provide insights into root, and direct, causes of an adverse event. These models provide empirical tests of causes of adverse events. We encourage the use of these methods by hospital risk managers.