References

Akshay Kumar, Rishabh Kumar. 2018. “Uplift Modeling : Predicting Incremental Gains.” 2018. http://cs229.stanford.edu/proj2018/report/296.pdf.

Bates, Douglas, Martin Maechler, and Ben Bolker. 2020. Linear Mixed-Effects Models Using ’Eigen’ and S4. https://cran.r-project.org/web/packages/lme4/index.html.

Bellamy, Rachel K. E., Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, et al. 2018. “AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias.” http://arxiv.org/abs/1810.01943.

Biecek, Przemyslaw, and Tomasz Burzykowski. 2019. Explanatory Model Analysis. https://pbiecek.github.io/ema/.

Bischl, Bernd, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, and Zachary M. Jones. 2016. “mlr: Machine Learning in R.” Journal of Machine Learning Research 17 (170): 1–5. http://jmlr.org/papers/v17/15-066.html.

Chen, Tianqi, and Carlos Guestrin. 2016. “XGBoost: A Scalable Tree Boosting System.” CoRR abs/1603.02754. http://arxiv.org/abs/1603.02754.

Conway, Jennifer. 2018. “Artificial Intelligence and Machine Learning : Current Applications in Real Estate.” PhD thesis. https://dspace.mit.edu/bitstream/handle/1721.1/120609/1088413444-MIT.pdf.

Din, Allan, Martin Hoesli, and Andre Bender. 2001. “Environmental Variables and Real Estate Prices.” Urban Studies 38: 1989–2000.

Esmukov, Kostya. 2020. “Python Geocoding Toolbox.” https://geopy.readthedocs.io/en/latest/#.

Friedman, Jerome. 2000. “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics 29 (November). https://doi.org/10.1214/aos/1013203451.

Gosiewska, Alicja, and Przemysław Biecek. 2019. “auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics.” The R Journal 11 (2): 85–98. https://doi.org/10.32614/RJ-2019-036.

Greenwell, B., B. & Boehmke. 2019. Gbm: Generalized Boosted Regression Models. https://cran.r-project.org/web/packages/gbm/index.html.

Guolin Ke, Thomas Finley, Qi Meng. 2017. “LightGBM: A Highly Efficient Gradient Boosting Decision Tree.” https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf.

Heyman, Axel, and Dag Sommervoll. 2019. “House Prices and Relative Location.” Cities 95 (September): 102373. https://doi.org/10.1016/j.cities.2019.06.004.

Hillstrom, Kevin. 2008. “The Minethatdata E-Mail Analytics and Data Mining Challenge Dataset.” 2008. https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html.

Jaroszewicz, S., and P. Rzepakowski. 2014. “Uplift Modeling with Survival Data.” In ACM Sigkdd Workshop on Health Informatics (Hi-Kdd’14). New York City, USA.

Jaskowski, Maciej, and Szymon Jaroszewicz. 2012. “Uplift Modeling for Clinical Trial Data.” In.

Kamishima, Toshihiro, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2012. “Fairness-Aware Classifier with Prejudice Remover Regularizer.” In Machine Learning and Knowledge Discovery in Databases, edited by Peter A. Flach, Tijl De Bie, and Nello Cristianini, 35–50. Berlin, Heidelberg: Springer Berlin Heidelberg.

Keras. 2020. Keras Website. https://keras.io/.

Kozak, Anna, and Przemyslaw Biecek. 2020. Local Variable Importance via Oscillations of Ceteris Paribus Profiles. https://cran.r-project.org/web/packages/vivo/index.html.

Krzysztof, Rudaś, and Szymon Jaroszewicz. 2018. “Linear Regression for Uplift Modeling.” Data Min. Knowl. Discov. 32 (5): 1275–1305.

Kuchumov, Artem. 2018. “Pyuplift Package - Documentation.” 2018. https://pyuplift.readthedocs.io/en/latest/index.html.

Larson, Jeff, Surya Mattu, Lauren Kirchner, and Julia Angwin. 2016. “How We Analyzed the Compas Recidivism Algorithm.” Edited by ProPublica.org. hhttps://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm.

Law, Stephen. 2017. “Defining Street-Based Local Area and Measuring Its Effect on House Price Using a Hedonic Price Approach: The Case Study of Metropolitan London.” Cities 60 (February): 166–79. https://doi.org/10.1016/j.cities.2016.08.008.

Lee, Josh Xin Jie. 2018. “Simple Machine Learning Techniques to Improve Your Marketing Strategy: Demystifying Uplift Models.” 2018. https://medium.com/datadriveninvestor/simple-machine-learning-techniques-to-improve-your-marketing-strategy-demystifying-uplift-models-dc4fb3f927a2.

Lundberg, Scott. 2018. “Interpretable Machine Learning with Xgboost.” 2018. https://towardsdatascience.com/interpretable-machine-learning-with-xgboost-9ec80d148d27.

Lundberg, Scott, and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” In.

Lundberg, Scott M., Gabriel G. Erion, Hugh Chen, Alex DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. 2019. “Explainable AI for Trees: From Local Explanations to Global Understanding.” CoRR abs/1905.04610. http://arxiv.org/abs/1905.04610.

Mostipak, Jesse. 2020. “Hotel Booking Demand.” https://www.kaggle.com/jessemostipak/hotel-booking-demand.

Park, Byeonghwa, and Jae Bae. 2015. “Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data.” Expert Systems with Applications 42 (April). https://doi.org/10.1016/j.eswa.2014.11.040.

R Core Team. 2018. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Rudin, Cynthia, Caroline Wang, and Beau Coker. 2018. “The Age of Secrecy and Unfairness in Recidivism Prediction.” http://arxiv.org/abs/1811.00731.

Rzepakowski, Piotr, and Szymon Jaroszewicz. 2012. “Decision Trees for Uplift Modeling with Single and Multiple Treatments.” Knowledge and Information Systems - KAIS 32 (August). https://doi.org/10.1007/s10115-011-0434-0.

scikit-learn. 2019a. GradientBoostingRegressor Documentation. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html.

———. 2020. Scikit-Learn Website. https://scikit-learn.org/stable/.

Selim, H. 2009. “Determinants of House Prices in Turkey: Hedonic Regression Versus Artificial Neural Network.” Expert Systems with Applications 36: 2843–52.

Sołtys, Michał, Szymon Jaroszewicz, and Piotr Rzepakowski. 2015. “Ensemble Methods for Uplift Modeling.” Data Mining and Knowledge Discovery 29 (November). https://doi.org/10.1007/s10618-014-0383-9.

Therneau, B., T. & Atkinson. 2019. Rpart: Recursive Partitioning and Regression Trees. https://cran.r-project.org/web/packages/rpart/index.html.

Verbeke, W., and C. Bravo. 2017. Profit Driven Business Analytics: A Practitioner’s Guide to Transforming Big Data into Added Value. Wiley and Sas Business Series. Wiley. https://books.google.pl/books?id=NCA3DwAAQBAJ.

Wright, Marvin N., and Andreas Ziegler. 2015. “Ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” https://doi.org/10.18637/jss.v077.i01.

Yi, Robert, and Will Frost. 2018a. “Pylift: A Fast Python Package for Uplift Modeling.” 2018. https://tech.wayfair.com/data-science/2018/10/pylift-a-fast-python-package-for-uplift-modeling/.

———. 2018b. “Pylift Package - Documentation.” 2018. https://pylift.readthedocs.io/en/latest/.

Zhao, Zhenyu, and Totte Harinen. 2019. “Uplift Modeling for Multiple Treatments with Cost Optimization.” http://arxiv.org/abs/1908.05372.