Causal Model Recap for 2012

This blog is partly about developing causal models to understand the world around us as it is happening in real time (the other part is simply about me venting steam while I read the popular press). It is based on reading Judea Pearl's book Causality: Models, Reasoning and Inference.  Prof. Pearl is a computer scientists studying artificial intelligence by asking how we can formalize for machine learning what humans do so easily: establish causal connections between events. Prof. Pearl's answer to the question is through the use of directed graphs.

One way to test Prof. Pearl's ideas is to look at current events and try to clarify arguments by developing causal models. Looking back on a year of doing this kind of testing here are the models, brief descriptions of the arguments and a link to the original blog postings.


Arguably, the most important event in 2012 was the unfolding Financial Crisis that started in 2007. A model I developed in January of 2012 (here) looked at the role of inequality (inequality has been increasing the US during the period of Neoliberalism to levels not seen since the Great Depression in the 1930--see the graph here) and the effect that a Wealth Tax might have on economic growth. The model indicates that inequality has a role in decreasing economic growth and that a wealth tax would have the opposite effect. The model is meant to confront the right-wing argument that inequality is necessary for economic growth.


I developed another model on a similar topic in February (here). In this model (click to enlarge) I pointed out that right-wing seems to make its arguments by reversing the actual direction of causation. The argument is that the Entitlement Society is creating economic problems while the direction of causation is in the other direction: economic forces (financial crises and globalization) are creating the need for increased Federal benefit spending. More data (and more models) will be needed to determine whether reverse-causation is right-wing strategy or simply confusion over the direction of arrows in positive feedback loops (viz., warm temperatures causing people to emit more CO2).

In May I picked up the topic of climate change (here) looking at a recent argument being made by the Climate Denial crowd, in this case Richard Lindzen. He was arguing that global temperature increase resulting from CO2 emissions (notice that the role of CO2 emissions in climate change has been admitted here) would trigger a negative feedback effect that would keep global temperature under control.

The negative feedback control loop he hypothesized involved the reduction of Cirrus Clouds. Since Cirrus Clouds are thought to play a role in creating the greenhouse effect (increasing global temperature) any reduction would act to control temperature.

This is an interesting and sophisticated argument from the Climate Change Deniers. There are many poorly understood feedback loops in the global climate system (some are reviewed here) and Cirrus Cloud formation is certainly one of them. In this case, there is little data that supports the argument.

The world climate system is obviously complex and some parts (local weather) are probably chaotic (more here). For us non-climate scientists, our best hope is to be able to develop the arguments as causal models and watch as the data accumulates.

In November (here) after Hurricane Sandy I looked at the role of climate change in severe weather formation and its consequences. The causal model above shows that the difference between air and sea temperature caused by climate change, in addition to increase atmospheric water vapor, will increase the intensity and consequences of hurricane flooding.

In a later post the same month (here) I added economic causes to the model showing how economic growth creates not only increased CO2 emissions but also increased coastal development. With greater coastal development and greater CO2 emissions, damage from Hurricanes can be expected to increase even more.

I would like to promise that I could develop causal models for the entire climate system at some point this year.  That would be really useful but also pretty premature. It will be along time before enough data is available to use in critiquing the models. On the other hand, the IPCC is scheduled to finalize the Fifth Assessment Report (AR5) in 2014. It would be useful to have a collection of models ready to use in reading AR5. And of course, the Subprime Mortgage Crisis is winding down and will continue to provide opportunities for casual modeling as various political parties and commentators try to put the monkey on someone else's back for the event.