Causal techniques

As discussed earlier, most data collection systems in the CPI place considerable emphasis on the "what," but provide little support for the "how" or "why." Causal analysis methods can be broadly divided into techniques which emphasize the structure of an accident and those which focus on causes. Structural techniques provide information on the "what" and "how," and the causal techniques enable the "why" to be investigated. 

Causal techniques (looking back to see how hazards and accidents might possibly be caused). 

Error analysis techniques can be used in accident analysis to identify the events and contributory factors that led to an accident, to represent this information in a clear and simple manner and to suggest suitable error reduction strategies. This is achieved in practice by identification of the causal event sequence that led to the accident and the analysis of this sequence to identify the root causes of the system malfunction. A discussion of accident analysis techniques is included in Chapter 6. 

A range of techniques is described for analyzing the structure of incidents and the causal factors involved. 

In the first of the following subsections, the data coDection approaches adopted in most CPI incident reporting systems will be described. The fact that these systems provide little support for systematically gathering data on underlying causes will provide an introduction to the later sections which emphasize causal analysis techniques. 

In the following sections, a number of methodologies for accident analysis will be presented. These focus primarily on the sequence and structure of an accident and the external causal factors involved. These methods provide valuable information for the interpretation process and the development of remedial measures. Because most of these techniques include a procedure for delineating the structure of an incident, and are therefore likely to be time consuming, they will usually be applied in the root cause analysis of incidents with severe consequences. 

An extension of the tree of causes, called variation diagrams (Leplat and Rasmussen, 1984) was developed to answer some of these criticisms. In this method, the Rasmussen stepladder model of human error (see Chapter 2) is applied to analyze causal factors at each node of the tree. A detailed example of the use of this technique is provided in Chapter 7 (Case Study 1). 

When the diagram is complete, the analyst proceeds through it to identify sets of events that were critical in the accident sequence. These critical events are then subjected to a further causal analysis using other techniques such as root cause coding, described below in Section 6.8.4. 

Microarray data cannot be analyzed by purely brute force techniques to generate a causal model of a set of biological processes because the data represents gene expression patterns that are only correlated with temporal processes of interest in the organism. Lander (1999) comments on this problem as follows ... 

One approach is to mesh all investigation and root cause analysis activities under one management system for investigation. Such a system must address all four business drivers (1) process and personnel safety, (2) environmental responsibility, (3) quality, and (4) profitability. This approach works well since techniques used for data collection, causal factor analysis, and root cause analysis can be the same regardless of the type of incident. Many companies realize that root causes of a quality or reliability incident may become the root cause of a safety or process safety incident in the future and vice versa. 

Checklist analysis tools can be a user-friendly means to assist investigation teams as they conduct root cause analysis.h) Each causal factor is reviewed against the checklist to determine why that factor existed at the time of the incident. The Systematic Cause Analysis Technique (SCAT)(9> is an example of a proprietary checklist tool. 

A number of deductive techniques require that the investigation team develop a tree. This is accomplished by reasoning to organize causal factors into a diagram (tree) and define their interrelationship. These logic... 

The technique for developing causal factor charts share a number of fundamental principles with MES and STEP. Basic principles for constructing sequence diagrams ) are given below. 

Many deductive investigation techniques use logic tree diagrams. A partial list of these methods includes fault tree analysis (FTA), causal tree... 

The simplest technique for identifying causal factors involves reviewing each event or condition on the timeline. The investigator repeatedly asks the following question ... 

If the answer is YES, that is, the incident would have heen prevented or mitigated, and it is a negative event or undesirable condition, then the fact is a causal factor. Generally, process safety incidents involve multiple causal factors. This technique is equivalent to step 15 in Figure 9-7. Once identihed, the causal factors become the candidates to undergo root cause analysis. 

The following is an analysis of one of these causal factors contractor operator (CO) falls asleep. The basic technique works with any of the predefined trees commonly used within the process industry. However, for the purposes of this example, a proprietary tool C) has been selected, and therefore the structure of the tree and the terminology used is specific to that tree. 

Among the multivariate statistical techniques that have been used as source-receptor models, factor analysis is the most widely employed. The basic objective of factor analysis is to allow the variation within a set of data to determine the number of independent causalities, i.e. sources of particles. It also permits the combination of the measured variables into new axes for the system that can be related to specific particle sources. The principles of factor analysis are reviewed and the principal components method is illustrated by the reanalysis of aerosol composition results from Charleston, West Virginia. An alternative approach to factor analysis. Target Transformation Factor Analysis, is introduced and its application to a subset of particle composition data from the Regional Air Pollution Study (RAPS) of St. Louis, Missouri is presented. 

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