Plans for second edition

The second edition, planned for early 2025 will focus on correcting errors, improving some explanations and adding minor edits overall. There may be a single new chapter.

Error correction

The most important plan will be correcting typos and errors based on the errata page.

  • typos, errors
  • improve unclear sentences
  • add a few lines of explanation when needed

New things, minor edits

We plan some (not many) new things, typically a few extra paragraphs, maybe a short new section.

Some ideas are

Part 1

Chapter Topic Idea
02 variable naming Add a few para on naming variables, some ideas and when it’s really important. Extend 2.U1 or add 2.U2
05 now short CS Maybe add a case study on estimating arrival time

Part 2

Chapter Topic Idea
08 Practice of standard errors Discuss special cases. One source is Gelman’s JE bit but we had thhought about countlessly.
10? Frisch-Waughn-Lowell theorem Add a short section on FWL (no proof), with a case study application. The key application will be a graph, ie show show scatterplot despite controls. Maybe use earnings. Or even add a new case study on Mankiw-Romer-Weil QJE growth regressions. Deepnote
10,21 dataviz Add coeffplots
10,22 p-values Show tables with p-values and stars, add a para discussion ref back to p-hacking + both have pros and cons
8 attenuation Add example for attenuation bias from Feodora Teti customs data paper, real policy implications

Part 3

Chapter Topic Idea
13 r vs python results Add a few para/section on discussing that results that are borne out of algos without a close solution, will vary across platforms
13 loss Price prediction model trade-offs, loss function Kayak
14 var imp for OLS For linear models in prediction, add a few para, new section on variable importance
14 ln OLS correction More on what smearing does, when it’s better to use other formula, bias, MAE vs RMSE
14 Quant reg If MAE is target, qreg is a way. MAE vs RMSE discussion
14 Correlated predictors In any predictive model (OLS, RF), when we have many predictors that are correlated, we have problems: varimp and interpretation. Ideas: PCA, groupings, drop
16 SHAP for ML For the machine learning bit, add SHAP in addition to VIP
16 ensemble for OLS For linear models in prediction, we can also have an ensemble model, ln+log
16 cloud comp Add run time in google colabs / amazon cloud for Table 16.4

Part 4

Chapter Topic Idea
19 SUTVA 2 para – Explicit about SUTVA
21 More on RDD A more detailed example on RDD, maybe even a short case study
21 Good vs bad control Two example stories with discussion on controls, confounders, mechanism and collider
24 Add a new DiD Event study, maybe add one of new DiD method using the same case study


We are open to suggestions! Plase make a suggestion for a minor change or a short addition you think would be helpful HERE. Also report errors, pls.