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.
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
|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|
|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|
|10,22||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|
|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|
|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.