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There are several different types of causes that can be identified in building models of cause and effect. Becoming aware of different types of causes and their interrelationships can assist in investigating possible linkages between social, political and economic factors, and changes in humanitarian conditions. 
A proximal cause is the event that immediately precedes the outcome of interest. There may be prior events that lead to the proximal cause. Such events that are more removed in the sequence of causal events are referred to as distal causes. By detailing steps, tracing backward from the outcome or forward from an initial event, causal pathways are defined. The steps from distal and proximal causes to an outcome of interest are collectively referred to as a chain of causation.14
By identifying proximal and distal causes, the process of causation can be better examined to define the order and relations among relevant variables. Some elements of a causal chain may turn out to be superfluous, and are eliminated from the model. More often, increasing knowledge leads to further specification of steps in a causal chain. The best causal models identify key events, their order of occurrence, and the character and magnitude of their influence on one another.
For example, a hypothesis that smoking causes cancer was first put forth in the 1940s by observing that smokers frequently got cancer, even though exactly how the causation occurred, biologically, was not yet known. It is now understood that smoking results in the inhalation of specific harmful chemicals that cause DNA damage when they come in sufficient contact with certain types of vulnerable cells. It is those DNA changes, in turn, that lead to cancer.
The same logic can be applied to sanctions. If it is believed that sanctions might lead to increased malnutrition among children in a particular situation, the next step is to test the validity of this assertion by seeking answers to questions such as: Do sanctions increase unemployment or impoverishment through increased costs and decreased sales? Do they lead to inflation and devaluation of the currency, causing food imports to cost more? Sanctions may lead to any and all of these things. The investigator needs to determine which of these factors may be operating in the country being examined. Relevant data for each of these variables can then be collected to determine if, and how much, it influences the next link in the chain.
The simplest models consist of direct causes, where event A leads straight to outcome B. Continuing with the example of possible linkages between sanctions and child malnutrition mentioned above (section 3.2.1), perhaps none of the possible direct causes mentioned is the cause of increased malnutrition. Perhaps instead the government raised food prices by holding back stocks, or sold food crops to buy weapons. Perhaps sellers in other countries, knowing that the sanctioned country had fewer possible sources of supply, inflated their prices. These would be indirect causes . . . indirect in that they operate through other, parallel (and possibly unanticipated) causal mechanisms (see also figure 6). By building models and examining data, investigators can determine how direct and indirect causes relate to one another and act together through a step-by-step chain.
Another example of an indirect cause is seen in the case of targeted UN sanctions against Liberia that were considered, but not imposed, during 2001. During a preassessment of possible humanitarian implications of the proposed sanctions on the timber, rubber sectors and the shipping registry, investigators asserted that the political debates on the imposition of sanctions alone had been sufficient to contribute to reduced confidence in the Liberian economy, which in turn affected local currency exchange rates and drove up prices of imported commodities.15 Even though in this case the sanctions were not imposed (timber sanctions were later imposed in 2003), it highlights the indirect and unintended impacts that may occur beyond the immediate target area of sanctions.
A causal model can also help to highlight which data will be needed for examining pathways of causation and making predictions about expected outcomes. In the most specific models where relevant quantitative information is available, a causal model can be used to attribute how much of an outcome is due to a set of events. It might be possible, for example, to establish that 40 per cent of a reduction in crop yield is caused by drought and 20 per cent is due to sanctionsrelated restrictions on the importation of fertilizer.
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