Bridges gap between modern machine learning methods and applied needs of economists, public health researchers, social scientists. Designed with students and practitioners in mind, introduces machine learning through causal inference. Offers a rigorous yet accessible roadmap for using data to answer real-world policy questions.
Bridges gap between modern machine learning methods and applied needs of economists, public health researchers, social scientists. Designed with students and practitioners in mind, introduces machine learning through causal inference. Offers a rigorous yet accessible roadmap for using data to answer real-world policy questions.
Mutlu Yuksel is a Professor of Economics at Dalhousie University, Canada, and an applied microeconomist whose research spans labor, health, and development. His recent work applies machine learning and high-dimensional data to complex policy questions. He has received teaching awards and co-founded the ML Portal to support research and training in social and health policy. Yigit Aydede is the Sobey Professor of Economics at Saint Mary's University, Canada, and an applied economist working at the intersection of econometrics, machine learning, and artificial intelligence (AI). He teaches data analytics and serves as Faculty in Residence at the Sobey School of Business and as an Affiliate Scientist at Nova Scotia Health. Aydede is also the co-founder of Novastorms.ai and the ML Portal, both focused on data-driven public policy and health research.
Inhaltsangabe
1. Introduction. 2. From Data to Causality. 3. Learning Systems. 4. Error. 5. Bias-Variance Trade-off. 6. Overfitting. 7. Parametric Estimation - Basics. 8. Nonparametric Estimations - Basics. 9. Hyperparameter Tuning. 10. Classification. 11. Model Selection and Sparsity. 12. Penalized Regression Methods. 13. Classification and Regression Trees (CART). 14. Ensemble Learning and Random Forest. 15. Boosting. 16. Counterfactual Framework. 17. Randomized Controlled Trials. 18. Selection on Observables. 19. Double Machine Learning. 20. Matching Methods. 21. Inverse Weighting and Doubly Robust Estimation. 22. Selection on Unobservables and DML-IV. 23. Heterogeneous Treatment Effects. 24. Causal Trees and Forests. 25. Meta Learners for Treatment Effects. 26. Difference in Differences and DML-DiD. 27. Synthetic DiD and Regression Discontinuity. 28. Time Series Forecasting. 29. Direct Forecasting with Random Forests. 30. Neural Networks & Deep Learning. 31. Matrix Decomposition and Applications. 32. Optimization Algorithms - Basics.
1. Introduction. 2. From Data to Causality. 3. Learning Systems. 4. Error. 5. Bias-Variance Trade-off. 6. Overfitting. 7. Parametric Estimation - Basics. 8. Nonparametric Estimations - Basics. 9. Hyperparameter Tuning. 10. Classification. 11. Model Selection and Sparsity. 12. Penalized Regression Methods. 13. Classification and Regression Trees (CART). 14. Ensemble Learning and Random Forest. 15. Boosting. 16. Counterfactual Framework. 17. Randomized Controlled Trials. 18. Selection on Observables. 19. Double Machine Learning. 20. Matching Methods. 21. Inverse Weighting and Doubly Robust Estimation. 22. Selection on Unobservables and DML-IV. 23. Heterogeneous Treatment Effects. 24. Causal Trees and Forests. 25. Meta Learners for Treatment Effects. 26. Difference in Differences and DML-DiD. 27. Synthetic DiD and Regression Discontinuity. 28. Time Series Forecasting. 29. Direct Forecasting with Random Forests. 30. Neural Networks & Deep Learning. 31. Matrix Decomposition and Applications. 32. Optimization Algorithms - Basics.
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