by Sebastian Benthall
A problem that’s coming up for me as a data scientist is the problem of textual causation.
There has been significant interesting research into the problem of extracting causal relationships between things in the world from text about those things. That’s an interesting problem but not the problem I am talking about.
I am talking about the problem of identifying when a piece of text has been the cause of some event in the world. So, did the State of the Union address affect the stock prices of U.S. companies? Specifically, did the text of the State of the Union address affect the stock price? Did my email cause my company to be more productive? Did specifically what I wrote in the email make a difference?
A trivial example of textual causation (if I have my facts right–maybe I don’t) is the calculation of Twitter trending topics. Millions of users write text. That text is algorithmically scanned and under certain conditions, Twitter determines a topic to be trending and displays it to more users through its user interface, which also uses text. The user interface text causes thousands more users to look at what people are saying about the topic, increasing the causal impact of the original text. And so on.
These are some challenges to understanding the causal impact of text:
- Text is an extraordinarily high-dimensional space with tremendous irregularity in distribution of features.
- Textual events are unique not just because the probability of any particular utterance is so low, but also because the context of an utterance is informed by all the text prior to it.
- For the most part, text is generated by a process of unfathomable complexity and interpreted likewise.
- A single ‘piece’ of text can appear and reappear in multiple contexts as distinct events.
I am interested in whether it is possible to get a grip on textual causation mathematically and with machine learning tools. Bayesian methods theoretically can help with the prediction of unique events. And the Pearl/Rubin model of causation is well integrated with Bayesian methods. But is it possible to use the Pearl/Rubin model to understand unique events? The methodological uses of Pearl/Rubin I’ve seen are all about establishing type causation between independent occurrences. Textual causation appears to be as a rule a kind of token causation in a deeply integrated contextual web.
Perhaps this is what makes the study of textual causation uninteresting. If it does not generalize, then it is difficult to monetize. It is a matter of historical or cultural interest.
But think about all the effort that goes into communication at, say, the operational level of an organization. How many jobs require “excellent communication skills.” A great deal of emphasis is placed not only on that communication happens, but how people communicate.
One way to approach this is using the tools of linguistics. Linguistics looks at speech and breaks it down into components and structures that can be scientifically analyzed. It can identify when there are differences in these components and structures, calling these differences dialects or languages.