Category: Uncategorized
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S-Learner Weaknesses: Causal Regularization and HTE Attenuation
When it comes to causal stuff in industry, a common pattern I observe is that people default to the S-learner. It’s the simplest of the meta-learners: one model, treatment as a feature, done. Given how powerful and convenient these methods are, this isn’t the most egregious mistake one can make. However, it’s important to understand…
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Warp Regression: Timing errors, warps, and a generative “terror”
For quite some time I’ve been interested in the idea of ‘time’ error, i.e. early, late, or in the time-series context, warped or unwarped. The machine learning field clings strongly to the idea using y-axis error or loss, but it’s not the only way to optimize. There is the whole error-in-variables field, for example, that…
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Stimmt So & German Practice
This month marks 10 years in Germany for me. It’s a big milestone! But, after all this time my German is still cringe. I’m in this real weird zone where I can understand fine, I can hold a conversation, but I make a grammar mistake about twice a sentence. I’m like a toddler with a…
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Hierarchical (Multilevel) Modelling
Hierarchical modeling is a powerful statistical technique to analyze nested or grouped data. It considers both global structure and individual characteristics, delivering more accurate and robust estimates. Hierarchical models outperform traditional machine learning models, providing lower error rates and better handle outliers, especially with small datasets.
Aaron Pickering 