This document contains links and excerpts from literature that provide critique on the concept "root cause" an addition to how the term is used. These references are from authors across a wide variety of fields (such as safety science, sociology, psychology, linguistics, and others) as well as many different domains (such as aviation, medicine, software, military, power generation, and others).

Dekker, S., Hollnagel, E., Woods, D., & Cook, R. (2008). Resilience Engineering: New directions for measuring and maintaining safety in complex systems. Lund University School of Aviation, 1, 1-6.

(p.6) Accidents were initially viewed as the conclusion of a sequence of events (which involved “human errors” as causes or contributors). This is now being increasingly replaced by a systemic view in which accidents emerge from the complexity of people’s activities in an organizational and technical context. These activities are typically focused on preventing accidents, but also involve other goals (throughput, production, efficiency, cost control) which means that goal conflicts can arise, always under the pressure of limited resources (e.g. time, money, expertise). Accidents emerge from a confluence of conditions and occurrences that are usually associated with the pursuit of success, but in this combination—each necessary but only jointly sufficient—able to trigger failure instead.

(p. 33) Accidents are not the result of an initiating (root cause) event that triggers a series of events, which eventually leads to a loss. Instead, accidents result from interactions among components that violate the safety constraints on system design and operation, by which feedback and control inputs can grow increasingly at odds with the real problem or processes to be controlled.

Carroll, J. S. (1995). Incident Reviews in High-Hazard Industries: Sense Making and Learning Under Ambiguity and Accountability. Industrial & Environmental Crisis Quarterly, 9(2), 175–197. doi:10.1177/108602669500900203

(p. 181) Root Cause Seduction

The identification of a root cause means that the analysis has found the source of the event and so everyone can focus on fixing the problem. This satisfies people’s need to avoid ambiguous situations in which one lacks essential information to make a decision (Frisch & Baron, 1988) or experiences a salient knowledge gap (Loewenstein, 1993). The seductiveness of singular root causes may also feed into, and be supported by, the general tendency to be over-confident about how much we know (Fischhoff,Slovic,& Lichtenstein, 1977).

However, theorists and researchers who have studied causal reasoning recognize that the assignment of causality is ambiguous or mistaken in several ways (Rasmussen, 1990). Most simple there is unlikely to be a single cause for any serious or surprising event. Instead,there is a confluence of behaviors, conditions, and circumstances that have developed over time (Reason, 1990), with precursors that have gone unheeded (Weick& Roberts,1993).

Johannesen, L., Sarter, N., Cook, R., Dekker, S., & Woods, D. D. (2012). Behind Human Error. Ashgate Publishing, Ltd..

Accidents in such systems occur when multiple factors together erode, bypass, or break through the multiple defenses creating the trajectory for an accident. While each of these factors is necessary for an accident, they are only jointly sufficient. As a result, there is no single cause for a failure but a dynamic interplay of multiple contributors. The search for a single or root cause retards our ability to understand the interplay of multiple contributors.

Vesel, C. (2012) Language bias in accident investigation. Masters Thesis, Lund University, Sweden.

This theory simplifies the idea of causation by limiting the search for cause to singular 'chains of events', which does not take into account the complexity of nature (e.g. fire), where multiple action chains may exist concurrently. One property of a complex system is emergence, which refers to phenomena that are new and not explicable by the properties of their components (2009; S. E. Page, 2011). This is different than resultant phenomena, which can be linked directly to other entities or events.

Leveson, (2011) “Applying Systems Thinking to Analyze and Learn from Events.”

(p. 9) consider 5 Whys. This technique is perhaps the most simplistic and it leads to the least amount of learning from events, but it provides an illustrative example of the problems in most current causal analysis techniques. Using 5 Whys, the investigation team questions "why" the inciden happened or "why" the unfavorable conditions existed. Specifically, the team selects an event associated with the incident, asks why this event occurred, and identifies multiple sub-events or conditions that gave rise to the event. For each of these sub-events or conditions, the team again asks why it occurred. The team records the sub- events or conditions as an event tree. The team then repeats this process five times to identify the root cause. The number five is arbitrary, of course, but the same limitations occur if the question is asked more times. Some problems with this approach include the fact that the results are not reproducible and consistent among investigation teams, systemic causes may not be identified, and a single cause is assumed along with one linear path to an event. Most important, the impacts of the potential ways to eliminate this cause are not evaluated.

Besnard, Denis, and Erik Hollnagel. “Some Myths about Industrial Safety,”

(p. 8) Root cause analysis is attractive because it is simple, but the validity of the method depends on three critical assumptions. First, it is assumed that events that happen in the future will be a repetition of events that happened in the past (Cf. the procedure myth in Section 3). Second, it is assumed that outcomes of specific events are bimodal; i.e., outcomes are either correct or incorrect. Third, it is assumed that the cause-effect relations can be exhaustively described. Using a bimodal causal model may be reasonable for purely technical systems where components are designed to provide a constant performance within well-defined limits until they fail for some reason. However, the bimodal view is not appropriate for the performance of complex socio-technical systems. Neither individual nor collective human performance is bimodal: it normally varies considerably but rarely fails completely.

Indeed, the very flexibility and adaptability of human performance is a unique contribution to safety (Reason, 2009). Yet this flexibility is disregarded when a root cause analysis points to a human as the origin of an unwanted event. The analysis only sees the failure (Cf. human error in Section 2) and fails to recognise that things go right and wrong for the same reasons. The possible elimination of the human activity that was deemed the cause of the outcome will unfortunately also prevent the far more frequent and far more likely positive contribution.

Wears, Robert L., and Christopher P. Nemeth. “Replacing Hindsight With Insight: Toward Better Understanding of Diagnostic Failures.” Annals of Emergency Medicine 49, no. 2 (February 2007): 206–9. https://doi.org/10.1016/j.annemergmed.2006.08.027.

(p. 2) Both hindsight and outcome bias have been convincingly demonstrated in a number of fields, including medicine. These biases are powerful and insidious and make it hard for historical analyses (such as root cause analysis or closed claim review) to yield useful understandings of accidents or adverse events.

Peerally, M. F., Carr, S., Waring, J., & Dixon-Woods, M. (2017). The problem with root cause analysis. BMJ quality & safety, 26(5), 417-422.

The first problem with RCA is its name. By implying—even inadvertently—that a single root cause (or a small number of causes) can be found, the term ‘root cause analysis’ promotes a flawed reductionist view.

Vincent, C. A. (2004). Analysis of clinical incidents: a window on the system not a search for root causes. Quality and Safety in Health Care, 13(4), 242–243. https://doi.org/10.1136/qshc.2004.010454

The term ‘‘root cause analysis’’, while widespread, is misleading in a number of respects. To begin with, it implies that there is a single root cause, or at least a small number. Typically, however, the picture that emerges is much more fluid and the notion of a root cause is a gross oversimplification.