First Course Ding

This page is the working table of contents for a crabbymetrics translation pass over the Peng Ding notebooks and data under ding_w_source/repl.

1 Current Batch

The first reviewable batch is already underway:

Each grouped section links out to the chapter-level pages already living under docs/ding/.

2 R Script Audit

The source folder includes both notebooks and companion R scripts. The R scripts are useful because they show which chapters have real executable examples and which chapters require functionality that is not yet in crabbymetrics.

  • Ported as executable Python pages: Chapters 1 through 8, Chapter 9 through the bridging ablation, Chapters 11 through 13, Chapters 21 and 23, and Chapter 27.
  • Source exists but no Python page exists yet: Chapters 10, 15 through 20, 22, 24 through 26, and Appendix A.
  • No chapter source exists for Chapter 14.
  • The main feature blockers visible in the R scripts are nearest-neighbor matching, Rosenbaum sensitivity analysis, local-polynomial RD / fuzzy RD, principal-stratification helpers, and a fuller mediation module.

The working rules for the port are:

  • use crabbymetrics estimators and primitives whenever the chapter logic allows it
  • keep external dependencies minimal: numpy, matplotlib, and pandas or polars only when a CSV or Stata read is genuinely required
  • avoid statsmodels, sklearn, scipy, linearmodels, and similar notebook-time dependencies in the translated docs unless a chapter is blocked on a missing crabbymetrics feature
  • prefer one Quarto page per chapter, with a small number of section pages to group completed chapters in the navbar

3 Implementation Batches

The rough order is:

  1. Randomized-experiment foundations and design-based inference.
  2. Observational studies and semiparametric estimators.
  3. IV and fuzzy-RD chapters.
  4. Principal stratification, mediation, and any residual appendix material.

This ordering matches the current library surface. The earliest chapters mostly need numpy, plotting, and some OLS or randomization-inference utilities. The middle chapters map onto BalancingWeights, AIPW, PartiallyLinearDML, EPLM, and AverageDerivative. The later IV chapters fit naturally on top of TwoSLS and GMM. The biggest likely blockers are matching, local-polynomial RD, principal stratification, and mediation.

4 Planned TOC

Chapter Source files Planned docs page crabbymetrics spine Minimal deps Notes
1 Chapter01CorrAssocSimpsons.ipynb, chapter01.R ding/ch01-correlation-simpson.qmd summaries + OLS where useful numpy, pandas, matplotlib implemented
2 Chapter02PotentialOutcomes.ipynb, chapter02.R ding/ch02-potential-outcomes.qmd numpy estimand algebra and simulation numpy, matplotlib implemented
3 Chapter03CREandFRT.ipynb, chapter03.R ding/ch03-cre-frt.qmd difference-in-means, OLS, permutation/randomization logic numpy, matplotlib implemented
4 Chapter04CREandNeyman.ipynb, chapter04.R ding/ch04-cre-neyman.qmd design-based variance calculations + simulation numpy, matplotlib implemented
5 Chapter05StratandPostStrat.ipynb, chapter05.R ding/ch05-stratification.qmd blocked Neyman/Fisher calculations, post-stratification arithmetic, blocked OLS numpy, pandas, matplotlib implemented
6 Chapter06RegadjRerand.ipynb, chapter06.R ding/ch06-regadj-rerand.qmd centered OLS, Lin-style adjustment, rerandomization simulation numpy, pandas, matplotlib implemented
7 Chapter07MatchedPairs.ipynb, chapter07.R ding/ch07-matched-pairs.qmd paired means, exact sign-flip randomization, pair-level regression adjustment numpy, pandas, matplotlib implemented
8 Chapter08UnifyingFisherNeyman.ipynb, chapter08.R ding/ch08-fisher-neyman.qmd randomization and repeated-sampling simulations numpy, matplotlib implemented
9 Chapter09BridgingFinitePopAndSuperPop.ipynb, chapter09.R ablations/bridging-finite-and-superpopulation.qmd OLS + stacked GMM numpy, matplotlib already implemented
10 Chapter10ObsStudiesSelBias.ipynb, chapter10.R ding/ch10-selection-bias.qmd observational-study simulation + balance diagnostics numpy, matplotlib source exists; no Python page yet
11 Chapter11Pscore.ipynb, chapter11.R ding/ch11-propensity-score.qmd Logit, propensity stratification, IPW truncation, balance checks, BalancingWeights numpy, pandas, matplotlib implemented
12 Chapter12DoubleRobustATE.ipynb, chapter12.R ding/ch12-double-robust-ate.qmd AIPW, Logit, OLS numpy, pandas, matplotlib implemented
13 Chapter13DoubleRobustATT.ipynb, chapter13.R ding/ch13-double-robust-att.qmd ATT outcome regression, odds weighting, doubly robust ATT, BalancingWeights numpy, pandas, matplotlib implemented
14 none in source none none none no chapter file present
15 Chapter15Matching.ipynb, chapter15.R ding/ch15-matching.qmd nearest-neighbor matching numpy, pandas, matplotlib source exists; blocked on matching and bias-adjustment support
16 Chapter16UnconfDifficulties.ipynb, chapter16.R ding/ch16-unconfoundedness.qmd overlap and model-misspecification simulations numpy, pandas, matplotlib source exists; feasible without new estimators
17 Chapter17Evalue.ipynb, chapter17.R ding/ch17-evalue.qmd analytic sensitivity summaries numpy, pandas, matplotlib source exists; feasible as a small sensitivity page
18 Chapter18SensitivityAnalysis.ipynb, chapter18.R ding/ch18-sensitivity-analysis.qmd omitted-confounding sensitivity calculations numpy, pandas, matplotlib source exists; likely wants reusable sensitivity helpers
19 Chapter19RosenbaumPvalues.ipynb, chapter19.R ding/ch19-rosenbaum.qmd matched-study sensitivity and p-values numpy, pandas, matplotlib source exists; blocked on matching-set support and Rosenbaum-style routines
20 Chapter20OverlapRD.ipynb, chapter20.R ding/ch20-overlap-rd.qmd overlap diagnostics and RD plots numpy, pandas, matplotlib source exists; local-polynomial RD is likely a new feature
21 Chapter21IVexperiments.ipynb, chapter21.R ding/ch21-iv-experiments.qmd Wald estimands, TwoSLS, compliance simulations, JOBS one-sided noncompliance numpy, pandas, matplotlib implemented
22 Chapter22IVmixtureDist.ipynb, chapter22.R ding/ch22-iv-inequalities.qmd IV bounds and mixture-distribution logic numpy, matplotlib source exists; mostly array algebra and plotting
23 Chapter23IVeconometrics.ipynb, chapter23.R ding/ch23-iv-econometrics.qmd TwoSLS, GMM, control-function OLS, Anderson-Rubin grid numpy, pandas, matplotlib implemented
24 Chapter24IVfuzzyRD.ipynb, chapter24.R ding/ch24-fuzzy-rd.qmd fuzzy RD as IV numpy, pandas, matplotlib source exists; local-polynomial RD is likely a new feature
25 Chapter25IVmendelian.ipynb, chapter25.R ding/ch25-mendelian-randomization.qmd ratio and multi-instrument TwoSLS numpy, pandas, matplotlib source exists; feasible with current IV machinery
26 Chapter26principalStratification.ipynb, chapter26.R ding/ch26-principal-stratification.qmd latent-strata models numpy, pandas, matplotlib source exists; principal-score weighting likely needs dedicated helpers
27 Chapter27mediationAnalysis.ipynb, chapter27.R ding/ch27-mediation.qmd Baron-Kenny mediation via sequential OLS regressions and explicit simulation DGPs numpy, pandas, matplotlib implemented as a transparent doc-level translation
A ChapterA.ipynb, chapterA1.R, chapterA2.R optional ding/appendix.qmd formulas and helper notes numpy, pandas source exists; low priority unless later chapters depend on it

5 Suggested Next Steps

The next concrete implementation batch should be:

  1. Chapter 10 to bridge the randomized and observational sections.
  2. Chapters 15 through 20 once matching and sensitivity helpers are scoped.
  3. Chapters 22, 24, and 25 as the remaining IV material.
  4. Chapter 26 only after the necessary latent-structure support exists; Chapter 27 now has a narrow Baron-Kenny translation, but a general mediation module remains future work.