Appendix A — References
Agresti, Alan. 2015. Foundations of Linear and
Generalized Linear Models.
John Wiley & Sons.
Albon, Chris. 2024. “Machine Learning
Notes.” https://chrisalbon.com/Home.
———. n.d. Machine Learning
Flashcards. Accessed April 1, 2024. https://machinelearningflashcards.com/.
Arel-Bundock, Vincent. 2024. “Marginal Effects
Zoo.” https://marginaleffects.com/.
Bai, Yu, Song Mei, Huan Wang, and Caiming Xiong. 2021.
“Understanding the Under-Coverage
Bias in Uncertainty
Estimation.” arXiv. https://doi.org/10.48550/arXiv.2106.05515.
Barrett, Malcolm, Lucy D’Agostino McGowan, and Travis Gerke. 2023.
Causal Inference in R. https://www.r-causal.org/.
Belkin, Mikhail, Daniel Hsu, Siyuan Ma, and Soumik Mandal. 2019.
“Reconciling Modern Machine Learning Practice and the
Bias-Variance Trade-Off.” Proceedings of the National Academy
of Sciences 116 (32): 15849–54. https://doi.org/10.1073/pnas.1903070116.
Biecek, Przemyslaw, and Tomasz Burzykowski. 2020. Explanatory
Model Analysis. https://ema.drwhy.ai/.
Bischl, Bernd, Raphael Sonabend, Lars Kotthoff, and Michel Lang, eds.
2024. Applied Machine Learning
Using Mlr3 in R. https://mlr3book.mlr-org.com/.
Bishop, Christopher M. 2006. Pattern Recognition and Machine
Learning. Information Science and Statistics. New York: Springer.
Boykis, Vicki. 2023. “What Are Embeddings?” http://vickiboykis.com/what_are_embeddings/index.html.
Brownlee, Jason. 2016. “Gentle Introduction to the
Bias-Variance
Trade-Off in Machine
Learning.” MachineLearningMastery.com. https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/.
———. 2021. “Gradient Descent With
AdaGrad From Scratch.”
MachineLearningMastery.com. https://machinelearningmastery.com/gradient-descent-with-adagrad-from-scratch/.
Bycroft, Brendan. 2023. “LLM
Visualization.” https://bbycroft.net/llm.
Carpenter, Bob. 2023. “Prior Choice
Recommendations.” GitHub. https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations.
causalml. 2023. “CausalML.” https://causalml.readthedocs.io/en/latest/index.html.
Cawley, Gavin C., and Nicola L. C. Talbot. 2010. “On
Over-Fitting in Model Selection
and Subsequent Selection Bias in
Performance Evaluation.” The
Journal of Machine Learning Research 11 (August): 2079–2107.
Chernozhukov, Victor, Christian Hansen, Nathan Kallus, Martin Spindler,
and Vasilis Syrgkanis. 2024. “Applied Causal
Inference Powered by ML and
AI.” arXiv. http://arxiv.org/abs/2403.02467.
Clark, Michael. 2021. “This Is Definitely Not All You
Need,” July. https://m-clark.github.io/posts/2021-07-15-dl-for-tabular/.
———. 2022. “Deep Learning for Tabular
Data,” May. https://m-clark.github.io/posts/2022-04-01-more-dl-for-tabular/.
Clark, Michael J. 2020. Practical Data
Science. https://m-clark.github.io/data-processing-and-visualization/.
———. 2022a. Bayesian Basics. https://m-clark.github.io/bayesian-basics/.
Cohen, Jacob. 2009. Statistical Power Analysis for the Behavioral
Sciences. 2. ed., reprint. New York, NY: Psychology Press.
Computing, UCLA Advanced Research. 2023. “FAQ:
What Are Pseudo R-Squareds?” https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-what-are-pseudo-r-squareds/.
DataBricks. 2019. “What Is AdaGrad?”
Databricks. https://www.databricks.com/glossary/adagrad.
Davison, A. C., and D. V. Hinkley. 1997. Bootstrap
Methods and Their Application. Cambridge
Series in Statistical and
Probabilistic Mathematics. Cambridge:
Cambridge University Press. https://doi.org/10.1017/CBO9780511802843.
Dobson, Annette J., and Adrian G. Barnett. 2018. An
Introduction to Generalized
Linear Models. 4th ed. New York: Chapman;
Hall/CRC. https://doi.org/10.1201/9781315182780.
Dunn, Peter K., and Gordon K. Smyth. 2018. Generalized
Linear Models With
Examples in R. Springer.
Efron, Bradley, and R. J. Tibshirani. 1994. An
Introduction to the Bootstrap. New York:
Chapman; Hall/CRC. https://doi.org/10.1201/9780429246593.
Facure Alves, Matheus. 2022. “Causal Inference for
The Brave and True —
Causal Inference for the Brave
and True.” https://matheusfacure.github.io/python-causality-handbook/landing-page.html.
Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. 2013.
Regression: Models, Methods and
Applications. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-34333-9.
Faraway, Julian. 2014. “Linear Models with
R.” Routledge & CRC Press. https://www.routledge.com/Linear-Models-with-R/Faraway/p/book/9781439887332.
Ferrari, Silvia, and Francisco Cribari-Neto. 2004. “Beta
Regression for Modelling Rates
and Proportions.” Journal of Applied
Statistics 31 (7): 799–815. https://doi.org/10.1080/0266476042000214501.
Fortuner, Brendan. 2023. “Machine Learning
Glossary.” https://ml-cheatsheet.readthedocs.io/en/latest/index.html.
Fox, John. 2015. Applied Regression
Analysis and Generalized Linear
Models. SAGE Publications.
Gelman, Andrew. 2013. “What Are the Key Assumptions of Linear
Regression? Statistical
Modeling, Causal Inference, and
Social Science.” https://statmodeling.stat.columbia.edu/2013/08/04/19470/.
Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki
Vehtari, and Donald B. Rubin. 2013. Bayesian Data
Analysis, Third Edition. CRC
Press.
Gelman, Andrew, and Jennifer Hill. 2006. Data Analysis Using
Regression and Multilevel/Hierarchical Models. Cambridge university
press.
Gelman, Andrew, Jennifer Hill, Ben Goodrich, Jonah Gabry, Daniel
Simpson, and Aki Vehtari. 2024. “Advanced Regression
and Multilevel Models.” http://www.stat.columbia.edu/~gelman/armm/.
Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2020. Regression and
Other Stories. 1st ed. Cambridge
University Press. https://doi.org/10.1017/9781139161879.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep
Learning. https://www.deeplearningbook.org/.
Google. 2023a. “Introduction Machine
Learning.” Google for Developers. https://developers.google.com/machine-learning/decision-forests.
———. 2023b. “Machine Learning
Google for Developers.” https://developers.google.com/machine-learning.
———. 2024a. “Classification: ROC Curve
and AUC Machine
Learning.” Google for Developers. https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc.
———. 2024b. “Reducing Loss: Gradient
Descent Machine
Learning.” Google for Developers. https://developers.google.com/machine-learning/crash-course/reducing-loss/gradient-descent.
Greene, William. 2017. Econometric Analysis - 8th
Edition. https://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm.
Hardin, James W., and Joseph M. Hilbe. 2018. Generalized
Linear Models and
Extensions. Stata Press.
Harrell, Frank E. 2015. Regression Modeling
Strategies: With Applications to
Linear Models, Logistic and
Ordinal Regression, and Survival
Analysis. 2nd ed. Springer Series in
Statistics. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-19425-7.
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2017.
Elements of Statistical Learning: Data
Mining, Inference, and Prediction. 2nd Edition. https://hastie.su.domains/ElemStatLearn/.
Heiss, Andrew. 2022. “Marginalia: A Guide to Figuring
Out What the Heck Marginal Effects, Marginal Slopes, Average Marginal
Effects, Marginal Effects at the Mean, and All These Other Marginal
Things Are.” Andrew Heiss. https://www.andrewheiss.com/blog/2022/05/20/marginalia/#what-are-marginal-effects.
Howard, Jeremy. 2024. “Practical Deep
Learning for Coders - Practical
Deep Learning.” Practical Deep
Learning for Coders. https://course.fast.ai/.
Hvitfeldt, Emil. 2024. “Feature Engineering
A-Z
Preface.” Feature Engineering A-Z. https://feaz-book.com/.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
2021. An Introduction to Statistical
Learning. Vol. 103. Springer Texts in
Statistics. New York, NY: Springer New York. https://doi.org/10.1007/978-1-4614-7138-7.
Koenker, Roger. 2000. “Galton, Edgeworth,
Frisch, and Prospects for Quantile Regression in
Econometrics.” Journal of Econometrics 95 (2): 347–74.
https://doi.org/10.1016/S0304-4076(99)00043-3.
———. 2005. Quantile Regression. Vol. 38. Cambridge university
press. https://books.google.com/books?hl=en&lr=&id=WjOdAgAAQBAJ&oi=fnd&pg=PT12&dq=koenker+quantile+regression&ots=CQFHSt5o-W&sig=G1TpKPHo-BRdJ8qWcBrIBI2FQAs.
Kruschke, John. 2010. Doing Bayesian Data
Analysis: A Tutorial
Introduction with R. Academic Press.
Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. 2019.
“Metalearners for Estimating Heterogeneous Treatment Effects Using
Machine Learning.” Proceedings of the National Academy of
Sciences 116 (10): 4156–65. https://doi.org/10.1073/pnas.1804597116.
Lang, Michel, Martin Binder, Jakob Richter, Patrick Schratz, Florian
Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff,
and Bernd Bischl. 2019. “Mlr3: A Modern
Object-Oriented Machine Learning Framework in R.”
Journal of Open Source Software 4 (44): 1903. https://doi.org/10.21105/joss.01903.
Lee, Jaehoon, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey
Pennington, and Jascha Sohl-Dickstein. 2017. “Deep
Neural Networks as Gaussian
Processes.” arXiv.org. https://arxiv.org/abs/1711.00165v3.
Lones, Michael A. 2024. “How to Avoid Machine Learning Pitfalls: A
Guide for Academic Researchers.” arXiv. https://doi.org/10.48550/arXiv.2108.02497.
Mahr, Tristan. 2021. “Random Effects and Penalized Splines Are the
Same Thing.” Higher Order Functions. https://tjmahr.github.io/random-effects-penalized-splines-same-thing/.
Masis, Serg. 2023. “Interpretable Machine
Learning with Python - Second
Edition.” Packt. https://www.packtpub.com/product/interpretable-machine-learning-with-python-second-edition/9781803235424.
McCullagh, P. 2019. Generalized Linear
Models. 2nd ed. New York: Routledge. https://doi.org/10.1201/9780203753736.
McCulloch, Warren S., and Walter Pitts. 1943. “A Logical Calculus
of the Ideas Immanent in Nervous Activity.” The Bulletin of
Mathematical Biophysics 5 (4): 115–33. https://doi.org/10.1007/BF02478259.
McElreath, Richard. 2020. “Statistical Rethinking:
A Bayesian Course with
Examples in R and STAN.”
Routledge & CRC Press. https://www.routledge.com/Statistical-Rethinking-A-Bayesian-Course-with-Examples-in-R-and-STAN/McElreath/p/book/9780367139919.
Molnar, Christoph. 2023. Interpretable Machine
Learning. https://christophm.github.io/interpretable-ml-book/.
Morgan, Stephen, and Christopher Winship. 2014. “Counterfactuals
and Causal Inference: Methods and
Principles for Social Research,
2nd Edition,” January. https://stars.library.ucf.edu/etextbooks/298.
Murphy, Kevin P. 2012. “Machine Learning:
A Probabilistic
Perspective.” MIT Press. https://mitpress.mit.edu/9780262018029/machine-learning/.
———. 2023. “Probabilistic Machine
Learning.” MIT Press. https://mitpress.mit.edu/9780262046824/probabilistic-machine-learning/.
Nelder, J. A., and R. W. M. Wedderburn. 1972. “Generalized
Linear Models.” Royal Statistical
Society. Journal. Series A: General 135 (3): 370–84. https://doi.org/10.2307/2344614.
Pearl, Judea. 2009. “Causal Inference in Statistics:
An Overview.” Statistics Surveys 3 (none):
96–146. https://doi.org/10.1214/09-SS057.
———. 2022. “Causal Inference: History,
Perspectives, Adventures, and
Unification (An Interview with
Judea Pearl).” https://muse.jhu.edu/pub/56/article/867087/summary.
Peng, Roger D. n.d. R Programming for Data
Science. Accessed April 15, 2024. https://bookdown.org/rdpeng/rprogdatascience/.
Penn State, Department of Statistics. 2018. “5.4 - A
Matrix Formulation of the
Multiple Regression Model
STAT 462.” https://online.stat.psu.edu/stat462/node/132/.
Pok, Wilson. 2020. “How Uplift Modeling Works
Blogs.” https://ambiata.com/blog/2020-07-07-uplift-modeling/.
Quantmetry. 2024. “MAPIE - Model
Agnostic Prediction Interval
Estimator — MAPIE 0.8.2 Documentation.”
https://mapie.readthedocs.io/en/latest/.
Raschka, Sebastian. 2022. Machine Learning with
PyTorch and Scikit-Learn. https://sebastianraschka.com/books/machine-learning-with-pytorch-and-scikit-learn/.
———. 2023a. Build a Large Language
Model (From Scratch). https://www.manning.com/books/build-a-large-language-model-from-scratch.
Rasmussen, Carl Edward, and Christopher K. I. Williams. 2005.
Gaussian Processes for Machine
Learning. The MIT Press. https://doi.org/10.7551/mitpress/3206.001.0001.
Ripley, Brian D. 1996. Pattern Recognition and
Neural Networks. Cambridge: Cambridge
University Press. https://doi.org/10.1017/CBO9780511812651.
Roback, Paul, and Julie Legler. 2021. Beyond Multiple
Linear Regression. https://bookdown.org/roback/bookdown-BeyondMLR/.
Rovine, Michael J, and Douglas R Anderson. 2004. “Peirce and
Bowditch.” The American Statistician 58
(3): 232–36. https://doi.org/10.1198/000313004X964.
Schmidhuber, Juergen. 2022. “Annotated History of
Modern AI and Deep
Learning.” arXiv. https://doi.org/10.48550/arXiv.2212.11279.
scikit-learn. 2023a. “1.16. Probability
Calibration.” Scikit-Learn. https://scikit-learn/stable/modules/calibration.html.
———. 2023b. “Nested Versus Non-Nested Cross-Validation.”
Scikit-Learn. https://scikit-learn/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html.
Shevchenko, Maksim. 2023. “Types of Customers — Scikit-Uplift
0.3.1 Documentation.” https://www.uplift-modeling.com/en/v0.5.1/user_guide/introduction/clients.html.
Simpson, Gavin. 2021. “Using Random Effects in GAMs
with Mgcv.” From the Bottom of the Heap, February. https://www.fromthebottomoftheheap.net/2021/02/02/random-effects-in-gams/.
StatQuest with Josh Starmer. 2019a. “Gradient
Descent, Step-by-Step.” https://www.youtube.com/watch?v=sDv4f4s2SB8.
———. 2019b. “Stochastic Gradient
Descent, Clearly
Explained!!!” https://www.youtube.com/watch?v=vMh0zPT0tLI.
———. 2021. “Bootstrapping Main
Ideas!!!” https://www.youtube.com/watch?v=Xz0x-8-cgaQ.
Turrell, Arthur, Pietro Monticone, Zeki Akyol, and Yiben Huang. 2024.
“Python for Data Science V1.0.1,”
January. https://doi.org/10.5281/ZENODO.10518241.
VanderPlas, Jake. 2016. “Python Data
Science Handbook [Book].”
https://www.oreilly.com/library/view/python-data-science/9781491912126/.
Weed, Ethan, and Danielle Navarro. 2021. Learning
Statistics with Python — Learning
Statistics with Python. https://ethanweed.github.io/pythonbook/landingpage.html.
Welchowski, Thomas, Kelly O. Maloney, Richard Mitchell, and Matthias
Schmid. 2022. “Techniques to Improve
Ecological Interpretability of
Black-Box Machine
Learning Models.” Journal of
Agricultural, Biological and Environmental Statistics 27 (1):
175–97. https://doi.org/10.1007/s13253-021-00479-7.
Wikipedia. 2023. “Relationships Among Probability
Distributions.” Wikipedia. https://en.wikipedia.org/w/index.php?title=Relationships_among_probability_distributions&oldid=1180084573.
———. 2024a. “Exponential Family.” Wikipedia. https://en.wikipedia.org/w/index.php?title=Exponential_family&oldid=1202463189.
———. 2024b. “Gradient.” Wikipedia. https://en.wikipedia.org/w/index.php?title=Gradient&oldid=1206147282.
Wood, Simon N. 2017. Generalized Additive
Models: An Introduction with
R, Second Edition. 2nd ed.
Boca Raton: Chapman; Hall/CRC. https://doi.org/10.1201/9781315370279.
Wooldridge, Jeffrey M. 2012. Introductory Econometrics:
A Modern Approach. 5th
edition. Mason, OH: Cengage Learning.
Zhang, Aston, Zack Lipton, Mu Li, and Alex Smola. 2023. “Dive into
Deep Learning — Dive into
Deep Learning 1.0.3 Documentation.” https://d2l.ai/index.html.