Finding cause and effect in human services
It is not a simple matter to establish that one event causes another*. We can observe one event following another. We can observe one event occurring with another. We cannot observe one event causing another. We can only infer one event causes another.
Key ingredients to inferring one event (X) causes another (Y) include:
There are ways we are prone to errors in inferring one thing causes another:
Some of the errors in our inferences arise because of the nature of our being human**:
In human services additional difficulties in inferring that X causes Y arise because:
So if we want to show that X causes Y in human services what can we do?
Firstly we need to ask ourselves:
If we need to be very certain, have time on our side and have resources - we probably need to undertake some rigorous research.
If we need to be moderately certain, don’t have time on our side and have minimal resources then what we can do is:***
Approach thinking about inferences in two ways:
We can look for the evidence by:
1. Ask observers - we can ask clients and staff about what causes what.
2. Ask wether what happened is consistent with what was intended to happen? For example if a program teachers parenting skills to improve parenting and parenting has improved we can ask whether the parenting skills that were taught are being used.
3. Check the timing of causes and effects makes sense. Are the causes before the effects?
4. Check whether the amount of ‘cause’ is related to the amount of ‘effect’. Does, for example more thorough teaching of parenting skills improve parenting more than less thorough teaching of the skills. Does an 8 week program work as well as the condensed four week version?
5. Identify what we think are the underlying causal mechanisms and the contexts in which they operate and check for patterns consistent and inconsistent with these mechanisms and the side effects of these mechanisms. For example we can develop a service model of the outcomes hierarchy and processes and think through the implications and see if we notice the patterns we would expect to see.
6. Make comparisons with other groups, for example a ‘comparison’ group or a ‘control’ group. To be most effective this step would build on the point above about developing the model of the underlying causal mechanisms and the contexts in which they operate.
7. Use statistical analysis to separate out the effects of extraneous variables on the inferred cause and effect relationship.
8. Make statistical models of how we expect the cause and effect relationships to work and see if they provide a plausible explanation for the data we observe (about X, Y and other related variables).
9. Design a rigorous research model to do the above with more rigour than we would be able to do in the normal course of our work.
Think things through.
Three things worth reading:
* David A de Vaus “Causation and the Logic of Research Design” Chapter 3 in Research Design in social Research Sage 2001 (19pp)
** Thomas Gilovich How we know what isn’t so. The fallibility of human reasons in everyday life. The Free Press 1993. (216pp)
*** E. Jane Davidson "Dealing with the causation issue" Chapter 5 in Evaluation Methodology Basics The Nuts and Bolts of Sound Evaluation, Sage 2005 (18pp)