A little-publicised development in statistics over the past two decades has been the admission of causality into respectable statistical discourse, spearheaded by the computer scientist Judea Pearl. Pearl’s definition (joint with Joseph Harpern) of causation (“X having setting x caused effect E”) has been formulated approximately as follows:
- X=x and E occurs.
- But for the fact that X=x, E would not have occurred.
Of course, Pearl is not the first person to think carefully about causality. He would certainly recognise the similarity to Koch’s postulates on demonstrating disease causation by a candidate microbe:
- No disease without presence of the organism;
- The organism must be isolated from a host containing the disease ;
- The disease must arise when the organism is introduced into a healthy animal;
- The organism isolated from that animal must be identified as the same original organism.
I was reminded of this recently in reading the Buddhist Assutava Sutta, the discourse on “dependent co-arising”, where this formula (that also appears in very similar wording in a wide range of other Buddhist texts) is stated:
When this is, that is;
This arising, that arises;
When this is not, that is not;
This ceasing, that ceases.