ms4ds
1
Outline
2
Introduction
3
Ethical data science
3.1
Motivating example: fair algorithms
3.1.1
A brief historical interlude
3.2
Philosophy of ethics
3.2.1
Meta-ethics
3.2.2
Normative ethics
3.3
Applied ethics
3.3.1
Belmont-Menlo principles
3.3.2
The five Cs framework
3.3.3
Ethical Guidelines for Statistical Practice
3.4
Causality and ethics
3.4.1
Causal models
3.4.2
Moral luck
3.5
Additional reading
4
Foundations of inference
4.1
Philosophy of statistics
4.1.1
Additional reading
4.2
Hypothesis testing background
4.2.1
Hypothesis formulation: a neglected topic in data science education?
4.3
Causality
5
Large scale testing
5.1
Multiple testing
5.1.1
Family-wise error rate
5.1.2
Special cases: groups and hierarchy
5.2
Selective inference
5.2.1
False discovery rate
5.2.2
Special cases: groups and hierarchy
5.3
Empirical Bayes approaches
6
Causal inference
6.1
A variety of treatment effects
6.2
Directed acyclic graphs and structural equation models
6.2.1
Common problems?
7
Foundations of regression
7.1
Philosophy of regression models
7.2
Linear regression
7.3
Generalized linear models and exponential families
7.4
Inference in regression models
7.5
Model diagnostics
8
Machine learning and high dimensional regression
8.1
Lasso and penalized regression
8.2
Generalized additive models
8.3
Trees, forests, boosting
9
Selective inference
9.1
Conditional approach
9.2
Debiasing approaches
10
Machine learning for causal inference
10.1
Double machine learning
10.2
Weight-based approaches
References
Published with bookdown
Modern Statistics 4 Data Science
Section 9
Selective inference
9.1
Conditional approach
9.2
Debiasing approaches