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Causal Diagramming

Causal diagramming is an analytical tool used to facilitate understanding of complex causal relationships among variables within a system. In rapid appraisal Diagnosis & Design exercises, team members often have different disciplinary perspectives on a problem. Use of causal diagramming helps avoid or resolve differences in the interpretation of the causal background of a problem; by displaying interrelationships between multiple contributing causes, fruitless discussions about what is the cause of a problem can be avoided.

In a Diagnosis & Design context, causal diagramming may be used to:

  • analyse the causal background to household supply problems
  • assist the team in its understanding of sustainability problems at the ecosystem level

Facilities available include:

  1. View Case Study
  2. View Guidelines to diagram construction
  3. View Causal Diagram

Guidelines to diagram construction

Causal diagramming is an art. Like most arts, it improves with practice. There is no single "correct" causal diagram for a given situation, there are only more or less useful ones. Although there are no hard and fast rules for the development of causal diagrams, there are certain conventions which, if observed, will greatly increase the usefulness of the tool.

The simplest use of causal diagramming is to depict the generative causal relationships responsible for a single problem. A set of such diagrams, one for each of the identified problems within the system, would be the result of this approach. However, since individual causal factors may contribute to the generation of more than one problem, interrelationships between separate diagrams should be shown whenever this will add to the clarity of the team's perception of significant causal relationships. This could be done as a second stage exercise, to bring about the necessary integration of partial diagrams developed in the first stage of the exercise. Perhaps the best strategy is to start with a diagram of the most significant or central problem within the system, and then judiciously expand the diagram as needed to elucidate significant connections with other problem complexes.

In deciding how to proceed at any stage in the use of causal diagrams, the criterion is always whether or not the contemplated use will add to the clarity of the team's causal understanding of a problem. Very complex diagrams attempting to describe all causal relationships in the background to a problem should generally be avoided, since "monster" diagrams will tend to confuse rather than clarify.

Step 1

Distinguish between different farming systems; different farming systems may experience different problems.

Step 2

For a particular farming system, identify the major landscape level and household level problems apparent from the case study material. Decide on a word or descriptive phrase to describe each problem. The convention is to move from causes on the right to effects on the left. Since the problem to be explained is the ultimate effect, it is entered at the extreme left side of the diagram (as an oval node).

Step 3

Identify the causal factors contributing to these problems, and decide on words or descriptive phrases for them. Enter these causal factors as nodes to the right of the problem. Leave trivial factors out. The idea is to construct a causal chain (right to left) of factors causing effects, which in turn, are causes of further effects, and so on. Draw arrows between nodes to illustrate a cause-effect relationship. There is a strict syntax to the diagram in which each arrow literally means "causes". Each pair of nodes connected by an arrow can, therefore, be read as a sentence in English. Thus, B <---- A translates as A causes B. This is a good way to check the validity of your diagram.

Step 4

Tidy up your diagram by rearranging the position of the nodes so as to display an orderly cause-effect progression from right to left. By this stage the progression will probably be more of a causal network than a simple causal chain. Arrows will usually point leftwards, except in the case of causal loops depicting feedback relationships.

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