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November 2024

August 2024

[LDSL#6] When is quantification needed, and when is it hard?
There are quite a few methods to convert rankings into numbers, such as Elo scores or item response theory. Different such methods generally yield…
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[LDSL#5] Comparison and magnitude/diminishment
In the previous post on LDSL, I argued that an important part of causal inference is to look at the larger things. This implies a method of comparison…
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[LDSL#4] Root cause analysis versus effect size estimation
In the conventional theory of causal inference, such as Rubin’s potential outcomes model or Pearl’s DAG approach, causality is modelled as a…
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tailcalled
[LDSL#3] Information-orientation is in tension with magnitude-orientation
Let’s say you are trying to make predictions about a variable. For example, maybe you are an engineer trying to keep track of how well the servers are…
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[LDSL#2] Latent variable models, network models, and linear diffusion of sparse lognormals
Where we last left off, I proposed that life cannot be understood through statistics, but rather requires more careful study of individual cases. Yet I…
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tailcalled
[LDSL#1] Performance optimization as a metaphor for life
Response to: The omnigenic model as a metaphor for life. In computer programming, there is a tradeoff between software that is quick and easy for humans…
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[LDSL#0] Some epistemological conundrums
When you deal with statistical science, causal inference, measurement, philosophy, rationalism, discourse, and similar, there’s some different questions…
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