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# 3.2 Causal thinking

Causal thinking is a way of linking activities or events together. A car mechanic explaining why your car won't start might tell you that a crack in the distributor head has caused the damp to get in which then caused a leakage of the current, which stopped the spark igniting the petrol. The same sort of reasoning lay behind the design of the engine in the first place: the petrol is mixed with air, then ignited, which causes an explosion, which pushes the crankshaft, which moves the wheels.

As you can see from this example, the three points I made about logical thinking apply to causal thinking too. To start with, it is objective; the political opinions of the car mechanic do not affect his explanation. As far as the reasoning being necessary is concerned, admittedly there is more scope for saying ‘it all depends’ – for example how damp the morning is, or how wide the crack is in the distributor head. But once you accept the premise of what damp does to ignition, then the conclusion will follow. Finally, there is the same sequencing ‘if a, then b, then c’ and so on to the conclusion.

Before moving on from the concept of causality, I want to raise the issue of thinking about chains of causes and consequences or multiple causes, as this an important feature of systems thinking.

When we say that A causes B (e.g. rising damp causes peeling wallpaper), or B is the consequence of A (e.g. peeling wallpaper is the consequence of rising-damp) we are also saying that if you alter rising-damp, then peeling-wallpaper will also alter. In other words, we are suggesting a way of altering B via A. This is why analysing patterns of causes and consequences can be useful when deciding upon actions. If you understand the network of direct and indirect causes that lead to B then, in principle, you have a large number of potential intervention points for changing B. Conversely, if you know all the direct and indirect consequences of your chosen intervention (e.g. change A), you can judge whether it will actually have the effect you want (e.g. change-in-B), and whether it is also likely to have other effects that you may or may not want.

Since systems is about developing understandings of situations that support practical change, causality is obviously a key area. Causality is not usually a simple matter of an isolated statement such as A-causes-B, however. You can trace causes back almost indefinitely if you want to. Consider:

Figure 2

The car crash didn't just happen spontaneously. Why did the car crash? Perhaps the driver lost control. That is

Figure 3

But why did the driver lose control? Perhaps

Figure 4

We can also go forward. What will be the further consequences? Perhaps

Figure 5

Why did the tyre burst in the first place? Perhaps it was some combination of a manufacturing defect, damage to the tyre wall caused by clumsy parking, and stress due to a particularly sharp turn:

Figure 6

So the event, tyre-burst, is the result of a set of causes that converge on it. Similarly, any event is likely to have a set of immediate consequences resulting from it; for example

Figure 7

The tendency of many people faced with a situation is to only think about single causes, or several causes in isolation, rather than consider the network of multiple causes.

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