Types of causes
used to identify
relationships
between
variables
|
There are several different types of causes that can be identified when building models of cause and effect. Becoming aware of these different types of causes and their interrelationships assists in exploring possible linkages between social, political and economic factors, and changes in humanitarian conditions. |
Proximal cause
|
A proximal cause is a cause that immediately precedes the outcome of interest. There may be prior events that lead to the proximal cause (see figure 1 below). |
| Distal cause
|
Such events that are more removed in the sequence of causal steps are referred to as distal causes. Causal pathways can be illuminated by tracing through intermediate steps, working backward from an outcome or forward from an initial event. |
Chain of
causation
|
The steps from distal and proximal causes to an outcome of interest are collectively referred to as a chain of causation. |
Direct and
indirect cause
|
The simplest models are composed of direct causes, where event A leads straight to outcome B. Indirect causes are those that operate through other, parallel causal mechanisms, or through additional intermediate steps. By building models and examining data, investigators can determine how direct and indirect causal variables relate to one another and act through a step-by-step chain, and which links in the chain are most susceptible to change. |
Necessary and
sufficient
conditions |
An event is sufficient to cause an outcome if no other events are required for the outcome to occur. There may be many sufficient events, any one of which could cause the outcome. Among a group of events, there may be one factor that must always be present for an outcome to occur. This is termed a necessary condition, in that the outcome cannot occur without this factor.
Any variable can be examined to determine if it is a proximal or distal cause of an outcome, and sufficient and/or necessary for the out come to occur. This process assists in identifying where the variable acts in the chain of causation, and the importance of the variable to the observed outcome. |
