Saturday, October 26, 2013

Complex Theories of Change: Recipes for failure or for learning?

The diagram below is a summary of a Theory of Change for interventions in the education sector in Country X. It did not stand on its own, it was supplemented by an extensive text description.

Its complex in the sense that there are many different parts to it and many interconnections between them, including some feedback loops. It seems realistic in the sense of capturing some of the complexity of social change. But it may be unrealistic if it is a prescription for achieving change. Whether it is the later depends on how we interpret the diagram, which I discuss below.

One way of viewing the Theory of Change is in terms of conditions (the elements in the diagram) that may or may not be necessary and/or sufficient for the final outcome to occur. The ideas of necessary and/or sufficient causal conditions are central to the notion of “configurational” models of causation, described by Mahoney and Goertz (2012) and others. A configuration is a set of conditions that may be either sufficient or necessary for an outcome e.g. Condition X + Condition T + Condition D + Condition P -> Outcome. This is in contrast to simpler notions of an outcome having a single cause e.g. Condition T -> Outcome.

The philosopher John Mackie (1974) argued that most of the “causes” that we talk about in everyday life are what are called INUS causes. That is, they are about a condition that is an Insufficient but Necessary part of a configuration of conditions but one which is Unnecessary but Sufficient for an outcome to occur. For example, smoking is a contributory cause of lung cancer, but it is neither necessary nor sufficient to get cancer. There are other ways of getting cancer and all smokers do not get cancer.

The interesting question for me is whether the above Theory of Change represents one or more than one causal configuration. I look at both possibilities and their implications.

If the Theory of Change represents a single configuration then each element, such as “More efficient management of teacher recruitment and deployment”, would be insufficient by itself, but a necessary part of the whole configuration. In other words, every element in the Theory of Change has to work or else the outcome won’t occur. This is quite a demanding expectation. The more complex this “single configuration” model becomes (i.e. by having more conditions), the more vulnerable it will becomes to implementation failure, because even if only part does not work, the whole process will fail. One saving grace is that it would be relatively easy to test this kind of theory. In any locations where the outcome did occur it would be expected that all elements would be present. If some were not, then the missing elements would not qualify as insufficient but necessary conditions.

 The alternative perspective is to see the above Theory of Change as representing multiple causal configurations i.e. multiple possible combinations of conditions, each of which can lead to the desired outcome. So any condition, again such as “More efficient management of teacher recruitment and deployment” may not be necessary under all circumstances. Instead it may be insufficient but necessary part of one of the configurations, but not the others. Viewed from this perspective, the Theory of Change seems less doomed to implementation failure, because there is more than one route to success.

However if there are multiple routes the challenge is then how to identify the different configurations that may be associated with successful outcomes. As it stands the current Theory of Change gives little guidance. Like many Theory of Change at this macro-level / sector perspective it tends towards showing “everything connected to everything”. In fact this limitation seems unavoidable, because with increasing scale there is often a corresponding increase in the diversity of actors, interventions and contexts. In such circumstances there are likely to be many more causal pathways at work. This view suggests that at such a macro level it might be more appropriate for a Theory of Change to initially have relatively modest ambitions and to limit itself to identifying the conditions that are likely to be involved in the various causal configurations.

The focus then would move to on what can be done through subsequent monitoring and evaluation efforts. This could involve three tasks: (a) Identifying where the outcomes have and have not occurred, (b) identifying how they differed in terms of the configuration of conditions that were associated with the outcomes (and absent where the outcomes did not occur). This would involve across-case comparisons. (c) Establishing plausible causal linkages between the observed conditions within each configuration. This would involve within-case analyses. Ideally, the overall findings about the configurations involved would help ensure the sustainability and replicability of the expected outcomes.

The Theory of Change will still be useful in as much as it successfully anticipates the various conditions making up the configurations associated with outcomes, and their absence. It will be less useful if it has omitted many elements, or included many that are irrelevant. Its usefulness could actually be measured! Going back to the recipe metaphor in the title, a good Theory of Change will have at least an appropriate list of ingredients but it will be really up to subsequent monitoring and evaluation efforts to identify what combinations of these produce the best results and how they do so (e.g. by looking at the causal mechanisms connecting these elements).

Some useful references to follow up:
Causality for Beginners, Ray Pawson, 2008
Qualitative Comparative Analysis, at Better Evaluation
Process Tracing, at Better Evaluation
Generalisation, at Better Evaluation


I have just read Owen Barder's review of Ben Ramalingam's new book "Aid on the Edge of Chaos" In that review he makes two comments that are relevant to the argument presented above:
"As Tim Harford showed in his book Adapt, all successful complex systems are the result of adaptation and evolution.  Many in the world of development policy accepted intellectually the story in Adapt but were left wondering how they could, practically and ethically, manage aid projects adaptively when they were dealing with human lives"
"Managing development programmes in a complex world does not mean abandoning the drive to improve value for money. Iteration and adaptation will often require the collection of more data and more rigorous analysis - indeed, it often calls for a focus on results and 'learning by measuring' which many people in development may find uncomfortable."
The point made in the last paragraph about requiring the collection of more data needs to be clearly recognised, as early as possible. Where there are likely to be many possible causal relationships at work, and few if any of these can be confidently hypothesised in advance, the coverage of data collection will need to be wider. Data collection (and then analysis) in this situation is like casting a net onto the waters, albeit still with some idea of where the fish may be. The net needs to be big enough to cover the possibilities.