Tag: ai
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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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The Rise of AI Foundation Models
One surprising development, at least to me, in AI is the growing proliferation of foundational models. Text? Sure, makes sense. There’s lots of data out there. Then images, audio, video somehow seemed a natural continuation. But now time series, tabular data, geospatial data, graphs? Foundation models built on public and synthetic data are producing workable…
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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…
Aaron Pickering 