Sewall Wright and John Tukey on Path Analysis in 1952

Papers from a symposium at The Biometrics Society (ENAR) Conference in 1952 at Iowa State College; Relevant to the history of causal inference.

tldr; Tukey had a lovely conference paper in 1952 explaining the deficiencies of correlation coefficients, articulating what made path analysis a distinct endeavor from regression, and outlining the problems with controlling for the colliders before that concept was invented. If you read the paper you will also get a taste of why Tukey once joined Charlie Winsor’s Society for the Suppression of Correlation Coefficients.

In 1952, Iowa State College and The Biometrics Society, ENAR (Eastern North American Region) sponsored a Biostatistics conference. I suspect this might have been the precursor to the annual ENAR conference. The work presented at this conference was later published as a series of book chapters in “Statistics and Mathematics in Biology” edited by Oscar Kempthorne, Theodore Bancroft, John Gowen and Jay Lush. One of the special sessions at this conference was titled “Correlation and Causation as Biometric Concepts” where both Sewall Wright and John Tukey were invited speakers. I was once reading David Freedman’s “A Case Study in Path Analysis” and he referenced results from a 1952 paper by Tukey. It was so unprecedented, that I just had to get the conference proceedings and get a copy of the paper.

In 2018, I tweeted out some excerpts from Tukey’s paper

John Tukey on Causation, Regression & Path Analysis in 1952.
He speaks favorably of Sewall Wright's path analysis and how structural regressions are different from predictive regressions. pic.twitter.com/pOvfMRjxF0

— Manjari Narayan (@NeuroStats) December 8, 2018

and Judea Pearl responded

Thanks Manjari for posting this paper. We should have mentioned Tukey in #Bookofwhy as a singularity (among statisticians) who stated publically that causation is NOT a species of correlation and that statistics can learn something from an outsider. Any link to the whole paper?

— Judea Pearl (@yudapearl) December 9, 2018

Here is a copy of Wright’s The Interpretation of Multivariate Systems and Tukey’s Causation, Regression and Path Analysis (excerpt below).

Causation, Regression & Path Analysis
John Tukey

When I began to prepare this paper a few weeks ago, I realized that the session title, "Correlation and Causation as Biometric Concepts," did not bring a very clear picture to my mind. So I got in touch with Professor Wright and learned roughly what he was going to discuss. It was no surprise that this was mainly path coefficient analysis, for I had heard of path coefficients repeatedly. The surprise came when I found that I did not know anything about path analysis, although after some study it seemed to be natural and useful.

After coming to the point where I thought I understood it moderately well, it occurred to me to wonder why I had not known about it before. The obvious answer was that it had not been used in the problems that I had been concerned with or discussed in the literature that I was used to reading. Was this because it was not useful? The evidence was strongly to the contrary. On further thought I came to the conclusion that the main reason for my ignorance was a tendency of many to confuse correlation and causation as biometric concepts.

What I shall try to do, therefore, is fourfold: (I) to set forth an opinion as to correlation, regression, and causation as methodological (and especially biometric) concepts and to relate path analysis to this point of view; (II) to discuss briefly the application of path analysis to inbreeding, pointing out the situations under which it may fail or require special attention; (III) to discuss briefly some of the applications of path analysis to observed data; and (IV) to discuss very briefly some topics which seem related to path analysis.

References

ENAR 1952 Announcement published in The American Statistician

*A previous version of this post originally appeared on my personal website.