dorsal/arxiv
View SchemaFisher's fundamental theorem and regression in causal analysis
| Authors | Steven A. Frank |
|---|---|
| Categories | |
| ArXiv ID | 2601.09011vv1 |
| URL | https://arxiv.org/abs/2601.09011 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Fisher's fundamental theorem describes the change caused by natural selection as the change in gene frequencies multiplied by the partial regression coefficients for the average effects of genes on fitness. Fisher's result has generated extensive controversy in biology. I show that the theorem is a simple example of a general partition for change in regression predictions across altered contexts. By that rule, the total change in a mean response is the sum of two terms. The first ascribes change to the difference in predictor variables, holding constant the regression coefficients. The second ascribes change to altered context, captured by shifts in the regression coefficients. This general result follows immediately from the product rule for finite differences applied to a regression equation. Economics widely applies this same partition, the Oaxaca-Blinder decomposition, as a fundamental tool that can in proper situations be used for causal analysis. Recognizing the underlying mathematical generality clarifies Fisher's theorem, provides a useful tool for causal analysis, and reveals connections across disciplines.
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"abstract": "Fisher\u0027s fundamental theorem describes the change caused by natural selection as the change in gene frequencies multiplied by the partial regression coefficients for the average effects of genes on fitness. Fisher\u0027s result has generated extensive controversy in biology. I show that the theorem is a simple example of a general partition for change in regression predictions across altered contexts. By that rule, the total change in a mean response is the sum of two terms. The first ascribes change to the difference in predictor variables, holding constant the regression coefficients. The second ascribes change to altered context, captured by shifts in the regression coefficients. This general result follows immediately from the product rule for finite differences applied to a regression equation. Economics widely applies this same partition, the Oaxaca-Blinder decomposition, as a fundamental tool that can in proper situations be used for causal analysis. Recognizing the underlying mathematical generality clarifies Fisher\u0027s theorem, provides a useful tool for causal analysis, and reveals connections across disciplines.",
"arxiv_id": "2601.09011",
"authors": [
"Steven A. Frank"
],
"categories": [
"stat.ME",
"q-bio.PE"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Fisher\u0027s fundamental theorem and regression in causal analysis",
"url": "https://arxiv.org/abs/2601.09011",
"version": "v1"
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