Friday, January 9, 2015

Distinguishing Cause From Effect Using Observational Data

In contrast, determining causal relationships is really hard. But techniques outlined in a new paper promise to do just that. The basic intuition behind the method demonstrated by Prof. Joris Mooij of the University of Amsterdam and his co-authors is surprisingly simple: if one event influences another, then the random noise in the causing event will be reflected in the affected event.

For example, suppose we are trying to determine the relationship between the the amount of highway traffic, and the time it takes John to drive to work. Both John’s commute time and traffic on the highway will fluctuate somewhat randomly: sometimes John will hit the red light just around the corner, and lose five extra minutes; sometimes icy weather will slow down the roads.

But the key insight is that random fluctuation in traffic will affect John’s commute time, whereas random fluctuation in John’s commute time won’t affect the traffic. By detecting the residue of traffic fluctuation in John’s commute time, we could show that traffic causes his commute time to change, and not the other way around.
Still, this method isn’t a silver bullet. Like any statistical test, it doesn’t work 100% of the time. And it can only handle the most basic cause-and-effect scenarios. In a three-event situation—like the correlation of ice cream consumption with drowning deaths because they both depend on hot weather—this technique falters.

- More Here

No comments: