I have my own interpretation on this topic, mainly from the data mining point of view.
Economists are interested in suggesting policies (i.e., suggest to people, "what to do"). Therefore, it is important to built models that assign causality. Computer scientists are rarely interested in the issue of causality. Computer scientists control the system (the computer) and algorithms can be directed to perform one way or another. In contrast, economists cannot really control the system that they study. They do not even know how the system behaves.
When a computer scientist proposes an algorithm, the main focus is to examine the performance of the algorithm under different settings of incoming data. How the (computer) system will behave is controlled. When an economist suggests a policy, it is highly unclear how the underlying (rational?) agents will behave. Therefore, it is important to figure out what exactly "causes" the behavior of the agents, and figure out what policies can change this behavior.
One area that gets closer to economics in this respect is the area of data mining and machine learning. Get the data, and learn how the underlying system behaves. For example, get data about credit card transactions and learn which of them are fraudulent. However, there is a significant difference in focus: Computer scientists are mainly focused on predictive modelling. As long as the system can "predict" the outcome on unseen data, things are ok. A black box with perfect predictive performance is great. Explanatory models are rarely the focus. In the best case, someone may want to understand the internals of the predictive model but even if the model can be understood (e.g., read the rules or the decision tree), these rules are rarely causal in nature.
Let me give you an example: Suppose that you are trying to predict price per square feet for houses. As one independent variable (feature) you add average size of the house in the area. What the predictive model will find? That places that have smaller houses also have higher price per square foot. Unexpected? Not really. Houses in urban areas are typically smaller and more expensive compared to the their suburban and rural counterparts.
For a predictive model, this information is absolutely sufficient; the average house size is a valuable feature for predictive purposes. Think however what would happen is someone was devising policy based on this feature. A house builder would try to build smaller houses in rural areas, hoping that the resulting prices would be higher. Or a politician in Manhattan would encourage construction of bigger apartments, since the experiments have shown that if average house size is increased, the prices will drop. Absurd? Yes.
Even funnier things can come up if someone uses country-wide data to predict demand for apartments using apartment prices. The result will show that increasing prices actually increases demand, even though we would expect the opposite. (Just the effect of prices increasing in places where there is higher demand.)
Predictive modeling can survive (or even thrive) by exploiting such strange correlations. A causal model that captures correlations and presents them as causes can wreak havoc.
So, an economist will try to build a model that will generate causal relationships. In the case above, a model based on supply and demand is more likely to result in a model that captures the true "causes" of increased apartment prices. A house builder can see these effects and make a more informed decision on how to build. Similarly, for a politician that is trying to encourage building more affordable housing.
An advantage of causal models over predictive models is that causal models are valid even if the underlying data distribution changes. Causal models are supposed to be robust, as long as the behavior of the agents remains the same. A predictive model works under the assumption that the "unseen" data follow the same distribution as the "training" data. Change the distribution of the unseen data, and any performance guarantee for the predictive models disappears.
Update 1: This is not an attempt to downgrade the importance of predictive models. Most of the results presented by Google after a query are generated using predictive modeling algorithms. You get recommendations from Amazon and Netflix as the outcome of predictive algorithms. Your inbox remains spam-free due to the existence of the spam filter, again a system built using predictive modeling techniques. It is too hard, if not impossible, to build "causal" models for these applications.
Update 2: An interesting example of a company
deriving policy based on their predictive model is American Express. They realized that the feature "customer buys in a 99c store" is correlated with higher delinquency rates. So, AmEx decided to decrease the credit limit for such customers. Of course, the result will be that potentially affected customers will stop visiting such stores, decreasing the value of this policy for AmEx. Furthermore, this action may cause even more economic stress to these customers that are now "forced" to buy from more expensive stores, and this may result in a much higher default rate for AmEx. This "unexpected" outcome is the effect of devising policy based on non-causal variables.
If AmEx had a variable "customer in economic distress", which arguably has a causal effect on default rates, then it would be possible to perform this action, without the ability of customers to game the system. However, since AmEx relied on a variable "customer buys in a 99c store" that is the outcome of the variable "customer in economic distress" it is possible for consumers to simply change their behavior in the face of economic distress.
RSS Feed
Twitter
