adjudication-sheets
Build human adjudication / hand-labeling sheets from LLM-pipeline data without evidence truncation. Use when: (1) preparing a…
pyfixest demeaner_backend="cupy64" (including its CPU fallback when cupy is absent) is NOT numerically identical to the default numba backend and does NOT drop fully-absorbed/collinear regressors the same way. Use when: (1) adding demeaner_backend="cupy64" to existing
Claude Code
$ npx -y skills add kennethkhoocy/applied-micro-skills --skill pyfixest-cupy64-absorbed-regressors --agent claude-code
Codex
$ npx skills add kennethkhoocy/applied-micro-skills --skill pyfixest-cupy64-absorbed-regressors -a codex -y
Cursor
$ npx skills add kennethkhoocy/applied-micro-skills --skill pyfixest-cupy64-absorbed-regressors -a cursor -y
Gemini CLI
$ npx skills add kennethkhoocy/applied-micro-skills --skill pyfixest-cupy64-absorbed-regressors -a gemini-cli -y
GitHub Copilot
$ npx skills add kennethkhoocy/applied-micro-skills --skill pyfixest-cupy64-absorbed-regressors -a github-copilot -y
OpenCode
$ npx skills add kennethkhoocy/applied-micro-skills --skill pyfixest-cupy64-absorbed-regressors -a opencode -y
Windsurf
$ npx skills add kennethkhoocy/applied-micro-skills --skill pyfixest-cupy64-absorbed-regressors -a windsurf -y
name: pyfixest-cupy64-absorbed-regressors description: | pyfixest demeaner_backend="cupy64" (including its CPU fallback when cupy is absent) is NOT numerically identical to the default numba backend and does NOT drop fully-absorbed/collinear regressors the same way. Use when: (1) adding demeaner_backend="cupy64" to existing pf.feols/fepois calls changes the printed coefficient table, (2) a regression report suddenly gains rows with absurd estimates (e.g. coef 435.8, SE 7106) for controls absorbed by the fixed effects, (3) diffing outputs before/after a backend change, or (4) anything parses a pyfixest text report by line position. author: Claude Code version: 1.0.0 date: 2026-07-18
Adding `demeaner_backend="cupy64"` to an existing `pf.feols()` call is treated as a pure performance switch, but it changes the output: regressors with zero within-FE identifying variation (fully absorbed by the fixed effects) that the default numba backend silently drops are RETAINED by the cupy64 path (also in its scipy/CPU fallback when cupy is not installed). They appear in the coefficient table as non-identified garbage (huge coefficient, huge SE).
Observed 2026-07-18 (Specialist Directors US, H5 re-baseline audit): adding the kwarg to 4 feols sites left the headline triple stable to 4 decimals (B1 diff 1.45e-6, SE diff 9.9e-6) but the text report grew from 366 to 390 lines — 24 new rows for absorbed controls (e.g. `event_x_lrisk = 435.8059`, SE `7106.3365`) under firm-year + director FE, where firm-year-level variables have no identifying variation.
1. Treat a backend change as a POTENTIALLY OUTPUT-CHANGING edit: diff the report and expect schema changes, not byte equality. Verify the coefficients of interest at ~4-decimal precision instead. 2. Never interpret retained absorbed-regressor rows as estimates; check within-FE variation before reading nuisance coefficients. 3. Never parse pyfixest text reports by line position; anchor on the variable name of the coefficient you need. 4. When byte-stable reports matter (regression-tested pipelines), pin the backend consistently everywhere rather than mixing backends across runs.
Re-run one model with and without the kwarg; compare: headline coef equal to ~1e-6, coefficient-row sets DIFFERENT (absorbed regressors present only under cupy64). That asymmetry confirms this behavior rather than a data change.
retention behavior, so "cupy isn't installed" does not make the kwarg inert.
reporting precision; this is a report-shape/nuisance-row issue, not a correctness issue for identified estimates.
Claude Code and Codex skills for empirical applied-microeconomics research: reproducibility auditing, LLM-assisted classification methods, event studies, data infrastructure (WRDS, Stata, pyfixest), and publication-grade tables, figures, and documents.
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