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This is where I probably spent too long listing things without prioritizing.
Start by acknowledging that a low R² may be expected for noisy, granular data like contribution per order, and that the goal is to improve predictive performance while preserving inferential validity. Then outline a systematic plan: diagnose the model, enrich features, consider alternative model forms, and validate rigorously with a focus on avoiding data leakage and overfitting.
Pro tip: Emphasize that improving R² is not always the goal—sometimes a low R² is acceptable if the model's coefficients are stable and interpretable for inference. Show you understand the trade-off between predictive power and causal inference.
Check for nonlinearity, heteroscedasticity, outliers, and influential points. Assess whether the low R² is due to missing key predictors, measurement error, or inherent noise.
Add relevant features from multiple sources (e.g., user demographics, order history, product attributes, temporal patterns) while avoiding leakage. Consider interactions and polynomial terms if justified.
Explore regularized regression (Ridge, Lasso), tree-based models (Random Forest, Gradient Boosting), or generalized additive models (GAMs) to capture nonlinearity. For inference, prefer models that maintain interpretability.
Use cross-validation, holdout sets, and regularization. For inference, ensure that any feature engineering or model selection is done within cross-validation folds to prevent optimistic bias.
If the goal is inference, prioritize coefficient stability and interpretability over raw R². If prediction is primary, consider a two-model approach: one for inference, one for prediction.
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No, obviously not, and I said that pretty quickly, but then stumbled explaining why.
Start by directly answering 'no' and explaining the bias-variance tradeoff and overfitting. Then describe a rigorous cross-validation procedure to demonstrate the effect, and finally discuss modeling alternatives that prioritize unbiased effect estimation over predictive performance.
Pro tip: Emphasize that for effect estimation, the goal is not maximizing R² but obtaining unbiased causal estimates; mention that adding covariates can introduce collider bias or post-treatment bias, which is a common pitfall in industry.
Distinguish between predictive performance (out-of-sample R²) and effect estimation (unbiased causal coefficients). State that adding covariates can improve prediction but may harm effect estimation if they are post-treatment or colliders.
Use k-fold cross-validation: split data, fit models with increasing covariates, compute out-of-sample R² on held-out folds. Plot R² vs. number of covariates to show potential overfitting and diminishing returns.
If the goal is effect estimation, consider methods like propensity score matching, inverse probability weighting, double machine learning, or instrumental variables. Discuss how these handle confounding without overfitting.
Highlight that more covariates increase variance and may reduce interpretability. In A/B testing, focus on pre-treatment covariates and avoid conditioning on post-treatment variables.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Nested CV I know well enough but explaining it out loud is harder than it sounds.
Start by framing the problem: iterating on model specs on the same validation set inflates false positives, so you need a disciplined evaluation protocol. Explain how nested cross-validation separates model selection from performance estimation, and how target-leakage tests catch subtle data contamination. Then tie it to p-hacking by describing how you'd pre-register hypotheses, limit iterations, and use statistical corrections or holdout sets to keep error rates honest.
Pro tip: Emphasize that the outer loop of nested CV must remain untouched until the very end—any peek at it for model selection turns it into a validation set and reintroduces p-hacking. Also mention that leakage tests should be automated and run as part of the CI pipeline, not just manually.
Pre-register the model specifications you'll test, the metric, and the nested CV structure (outer folds for performance, inner folds for hyperparameter tuning). This prevents ad-hoc changes that inflate Type I error.
Use an outer loop (e.g., 5-fold) to estimate generalization performance and an inner loop (e.g., 3-fold) for hyperparameter selection. Only the outer loop's held-out predictions are used for final evaluation, ensuring no information leaks from selection to assessment.
Systematically check for leakage by: (a) verifying that no future information is used in features, (b) testing if a model trained on shuffled targets performs better than chance, and (c) using time-based splits if data is temporal. Automate these checks to run on every pipeline change.
Limit the number of model specifications tested and apply corrections like Bonferroni or Benjamini-Hochberg to p-values from statistical tests. Alternatively, use a separate holdout set that is only touched once at the end.
Keep a log of all model iterations, including those that failed, and report the final model's performance on the untouched outer loop. This transparency demonstrates rigor and helps stakeholders trust the results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was my favorite part of the whole conversation.
Start by distinguishing between estimating an average treatment effect (ATE) and making individual-level predictions. Explain that low R² is acceptable for ATE if the treatment effect is precisely estimated, but problematic for individual predictions because low R² indicates high unexplained variance. Use examples from A/B testing and product analytics to illustrate.
Pro tip: Emphasize that in A/B testing, the goal is often to estimate the average effect, not to predict individual outcomes; thus, low R² can still yield valid inferences if the experiment is well-powered. However, for personalization or targeting, low R² means predictions are unreliable and may lead to poor decisions.
Determine whether the objective is to estimate an average treatment effect or to make individual-level predictions. This distinction is crucial for interpreting R².
Explain that R² measures the proportion of variance in the outcome explained by the model. For ATE, the focus is on the coefficient of the treatment variable, not overall predictive power.
Low R² is acceptable for ATE when the treatment effect is estimated with low bias and high precision (e.g., in randomized experiments). The average effect can be valid even if individual outcomes are noisy.
Low R² becomes a problem for individual predictions because it indicates that the model explains little of the variance, leading to unreliable predictions and potentially harmful decisions.
Relate to Instacart scenarios: for example, estimating the average impact of a promotion on orders (low R² okay) versus predicting which customers will respond to a promotion (low R² problematic).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.