Predictive Models

This page documents some exploratory thinking about how we can develop predictive models of what happens in complex development projects

Predictive and explanatory models are both useful

We can make a distinction between predictive and explanatory models. A predictive model may say that where conditions A+B+C exist then we will find outcome Y. But there may or may not be any causal mechanisms connecting A+B+C. That is not necessarily a problem. For example, if the model described conditions in a stock market, we could still make money from this predictive model. But if we wanted to engineer these conditions to be present in order for the outcome to occur we would probably need to know something about the underlying causal processes. A predictive model could be developed into an explanatory model, if we did some homework on what was happening inside the cases we used to develop the predictive model.  Different kinds of models may be useful for different kinds of people and situations.

Explanatory and predictive models

We need models that can capture complexity

One option is to use what are called “multiple conjunctural causation” models.  This is the approach used by Qualitative Comparative Analysis (QCA), but which can also be used as the basis for other approaches to developing explanatory and/or predictive models.

An example would be a model that says A+B+C leads to Y or A+D+F leads to Y or notA +M+N leads to Y

Here there are three configurations involving different combinations of seven conditions (present or absent), each of which can lead to Y. This feature is called equifinality.

Another feature of these models is that they can be asymetrical. The cause of Y may not simply be the absence of the conditions above. It may be some other combination altogether. Example. I may be motivated to go to a conference, but the reason I dont go to a conference may not be lack of motivation, but another pressing engagement

Associated with this approach are the distinctions made between sufficient and necessary conditions

  • A condition may be sufficient and necessary
  • A condition may be sufficient but not necessary
  • A condition may be necessary but not sufficient
  • A condition may unnecessary and insufficient, but still important. In the model above A was a necessary part of two configurations,  either of which that was sufficient but not necessary to lead to the outcome

How we can develop predictive models

There are quite a few alternative ways of developing good predictive models, given the availability of a data set with x different attributes and one outcome of interest that needs to be predicted  . Here the ones that I am aware of, and have or am experimenting with:

  1. Manual hypothesis-led searches
  2. Exhaustive searches of all possible combinations of attributes
  3. Algorithm based searches, including
    1. QuineMcCluskey algorithm, as built into QCA software. Here is an introductory page I  wrote on QCA
    2. Decision Tree algorithms (there are more than one)
    3. Genetic algorithms
  4. Ethnographic/participatory methods that can be used for predictive purposes
    1. Ethnographic decision tree models, as developed by Gladwin in 1989
    2. Hierarchical Card Sorting

 

Update 2016 06 16: Over the last six months I have been working with Aptivate to develop an Excel package of tools called EvalC3, designed to enable the exploration and evaluating complex causal configurations. You can find more about EvalC3 via this supporting website. You can also request a copy of EvalC3 if you would like to help with testing out the software. It is free and available under a Creative Commons Attribution Non-Commercial Share Alike licence (contact me on rick.davies@gmail.com)

Here is a short (9 min) video explaining how it works

 

 

 

 

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