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ML/Statistics

A collection of 158 issues

The Ties That Bound

The previous post described a conservative record-linkage pipeline: accept only high-confidence matches, flag ambiguous cases, and leave the rest unmatched. That is the right approach when false matches are more costly than missed ones, but it means the matched subset is selected on ease of matching, and estimates from it

Recommended Treatment

The effect of a treatment on an outcome depends on three things: the kind of treatment, the kind of person receiving it, and the context in which it is delivered. A ranking change and a button color change work through different mechanisms. A heavy user and a new user will

No Logbook, No Problem: Recovering LATE When You Can't Observe Compliance

Suppose you run a randomized experiment with imperfect compliance. You assigned treatment, measured outcomes, and now you want the LATE. The Wald estimator says divide the ITT by the compliance rate, $\pi = E[D_i \mid Z_i = 1] - E[D_i \mid Z_i = 0]$, and you'

Regression to the Mean Model

Shuffle your XGBoost training data and you can get a different model. Not different hyperparameters or different features, just a different row order. With tree_method='hist' and multiple threads, the parallel quantile sketch that determines bin boundaries depends on how rows are chunked across workers (see stableboost

Comparative Advantage: Calibrated Pairwise Win Rates from LLM Judge Scores

Causal Judge Evaluation (CJE; Landesberg, 2025) formalizes a workflow that is already widespread in practice: score every response with a cheap LLM judge, label a small slice with a trusted oracle, learn the judge-to-oracle mapping, and evaluate at scale with calibrated uncertainty. However, calibration is only as informative as the

Simulating and Evaluating Agentic Systems

Most teams building agentic systems know they need some way to test multi-turn conversations. The agent interprets ambiguous input, picks tools, maintains state, and has to land in the right place at the end. You can't eyeball that. So people reach for what they already know. The most

Out-of-Context: Constrained Tool Based Exploration of Context

Longer context windows have not come at the back of a solution for "context rot." Recursive Language Models (RLMs) take inspiration from out‑of‑core data processing, where systems handle datasets larger than memory by deciding what to load. The paper proposes solving the context rot problem by

Targeted Model Editing

Pretrained generative models (LLMs, text-to-image, etc.) are used as general-purpose engines and then "aligned" toward specific goals: lower toxicity, higher truthfulness, removal of certain objects or styles, domain specialization, and so on. In practice, we rarely want to rebuild the whole model. The real objective is more surgical.

Shipping Fuzzy‑Joined Data

A growing share of empirical work relies on "data products" that integrate multiple administrative and survey sources via fuzzy joins. Researchers typically receive only the final merged panel and treat each cell as if it were directly observed. But upstream, the data producer has made substantive decisions—how

Chaotic Flows: LLMs in Low‑Tolerance Workflows

When you add an LLM to a workflow, you change both the upside and the downside. The net effect is easiest to express as a shift in the workflow's value: $$ \Delta \text{Value} = \Delta \text{Benefit} - N \sum_i f_i \cdot \Delta p_i \cdot L_