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ML/Statistics

A collection of 147 issues

What Problem Is Traditional RAG Solving?

RAG is a solution to a specific technical problem. It helps models trained on broad public text answer questions about specialized domain content without a training loop. A solution to a technical problem is, however, not the same as a solution to a user problem. To identify cases where traditional

Conscious Decoupling: Separating Budgets For Thought and Action

Current LLM services bundle model intelligence with supporting infrastructure into fixed tiers. When you purchase access to GPT‑5 Thinking, Claude Opus 4.1, or similar offerings, you receive a package: some amount of model capability paired with some unspecified quantity of supporting compute. This bundling serves a purpose. It

Testing LLMs: Engineering Confidence Without Certainty

Software testing has always relied on a fragile assumption: that we can enumerate test cases that represent production behavior. We test specific inputs, verify outputs, and trust that production will behave similarly. This works until it doesn't. Search ranking degrades as user behavior evolves. Heuristics fail when traffic

What Problem is DSPy Solving

Follow up to: Not so Prompt: Prompt Optimization as Model Selection The practical problem is to choose a prompt (or a set of prompts) that perform the best on a task in the "real world" while respecting constraints, e.g., the output must parse, latency should stay below

The Double Descent of Standard Errors

Since the last essay, I have been percolating over whether double descent also means smaller s.e. for HTE. Estimating heterogeneous treatment effects involves two goals: accurate out-of-sample prediction of $\hat{\tau}(x)$ and valid inference for functionals like average treatment effects and policy values. Standard practice constrains model complexity

Testing Distributional Implications of LATE

Many randomized encouragement designs have imperfect compliance, where only a fraction of people comply with their assignment. Examples include phone-bank get-out-the-vote (GOTV) campaigns and draft lotteries like the Vietnam Draft Lottery. In these settings, it is common to use instrumental variable (IV) regression for analysis. Instrumental variables identify the Local

Unbiased regression with costly item labels

Consider a common scenario: you observe how units (users, devices, firms) interact with items (websites, products, apps), and each item has an unknown binary trait (is it harmful? is it premium content?). You want to understand how these traits relate to unit characteristics through regression, but labeling items is expensive.

A Program of Experiments With Risk, Context, and Organizational Constraints

An experimentation program is a portfolio problem: at each time $t$, given what we currently know and today's constraints (backlog, traffic budget, test and implementation costs), we must choose a slate of ideas to test, decide how much traffic to allocate, and determine which experiments to continue, stop,

The Elephant in the Sampling Frame: Lessons from Basu's Parable

In Basu's circus story (PDF), the owner wants to estimate the weight of fifty elephants and suggests weighing Sambo, who looked “average” three years ago, and reporting $50y$. A statistician counters with a contrived sampling plan: choose Sambo with probability $99/100$ and pick any of the other

Bandwidth Selection Without Grid Search

Bandwidth selection is often framed as "scan a grid and pick the CV minimum." For common univariate problems, you can do better. The leave‑one‑out (LOO) cross‑validation objectives admit closed‑form derivatives in the bandwidth $h$. With analytic gradients (and Hessians when useful), a one‑dimensional