Understanding Causation

1. It is because of such cause-and-effect connections that an intervention intended to change the state of some system element (implementation of malaria eradication program) can eventually alter many other system elements (such as mosquito population, migratory human and livestock populations, stock of minerals and forests cover and vegetation type etc.).
2. Here are examples of such relationships, indicated by drawing an arrow between the two.

Amount of food intake -> Body Weight
Number of saplings planted -> Number of tree

3. Thus, the causal links shown in point 2 above can be read as “amount of food intake causes the body weight to change”, and “the number of saplings planted causes number of trees to change.”
4. When studying a situation from a system’s perspective by using the Cause-Effect tool, the focus is to look for and capture the “cause-effect” (or causal) relationships between system elements. Some elements may not be connected in cause effect relationships, while others are.
5. In some cases, it may appear that elements have a cause-and-effect relationship, but further investigations may reveal that there is no real cause effect relationship. On the other hand, no cause-effect may be visible, but further investigations may reveal that such a relationship is in fact present.

Note that correlation does not necessarily mean causation.

A correlation exists between two variables, when one variable changes, the other variable also changes.

Causation is when a change in one variable causes a change in the other variable.

7. In the world, we find that most elements are connected with several other elements and it is possible that each one of them could be affected by several others.

While drawing causal links between two elements, the influence of all the other elements must not be mixed up together. For instance, one can say that the state of forest resources changed because of government policies.

This is not untrue; however, aggregating several influences together could give us only a limited understanding and short insight to intervene in the system. Therefore, each one of the influences should be considered independently. (See Figure 1.11)

Also, only direct influences must be considered. For instance, because greater number of learners were willing to plant saplings, greater number of saplings were planted and thus it caused number of trees to change. While describing this, one may get tempted to establish a link between number of learners willing to plant sapling and number of trees. This must be avoided. Instead, a series of influences can be drawn, by considering each relationship independently and in isolation.

See a similar example shown in Figure 1.12. The correct mutual interplay of all influences is found later as we go on developing the diagram.  The loss of canopy cover in the forests of Uttara Kannada district in India caused the spread of the invasive plant species Eupatorium, which led to an increase in the risk of forest fires.