References
Prediction, Inference, and Causality
Prediction, Inference, and Causality I
1
The Social Pressure Experiment
One-Sample Problems
2
Sampling
3
Homework 0: Basic Descriptive Statistics
4
Point and Interval Estimates
5
Homework: The Idea of Calibration
6
Calibrating Interval Estimates with Binary Observations
7
Homework: Calibration with Binary Outcomes
8
Calibrating Interval Estimates using the Bootstrap
9
Random Variables and Moments
10
The Standard Deviation is Standard
11
Homework — Random Variables, Moments, and the Standard Deviation
12
Normal Approximation
13
Homework 4: Sample Size Calculation and Coverage
14
Probability Review
15
Variance, Standard Deviation, and Sample Size
16
Homework 5: RVs, Markov’s Inequality, Chebyshev’s Inequality, Biased Estimators
17
Bias and Coverage
18
Homework: Comparing Estimators
19
Practice Midterm 1
Distributional Approximation
20
Meeting 14: Why the CLT Works — Stein’s Method
21
Meeting 15: When Normal Breaks Down — Poisson Approximation and Quality Control
22
Meeting 16: Stein’s Method and Poisson Approximation
Two-Sample Problems
23
Comparing Two Groups
24
Homework 7: Bias with Two Groups
25
Homework 8: Sample Size Calculation for Two-Sample Problems
26
Potential Outcomes and Randomization
27
Homework 9: Causality
28
Sampling and Randomization
29
Observational Studies and Confounding
30
Models
31
Practice Midterm 2
32
Causality Questions for Unit 2 Exam
Regression Analysis
33
Summarizing Trends involving Many Groups
34
Multivariate Analysis and Adjustment
35
Homework: Covariate Shift
36
A Game of Telephone
37
Inference for Adjusted Comparisons
38
Homework: Point Estimation for Complex Summaries
39
Conditional Randomization
40
Inverse Probability Weighting
41
Homework: IPW and Estimating Treatment Effects
42
Imagining Randomization in Observational Studies
43
Practice Midterm 3
Tools in Signal Processing
44
Models and Loss Functions
45
Empirical Risk Minimization and the Union Bound
46
Homework: Linear Models and Basis Functions
47
Signal Processing
48
Linear Models 2: Polynomials and Splines
49
Least Squares in
R
50
Meetings 22-23: Model Selection and Aggregation
Linear Models
51
Least Squares Regression in Linear Models
52
Least Squares in
R
53
The Behavior of Least Squares Predictors
54
Homework: Misspecification and Bias
55
Lecture 12: Linear Algebra Perspective
56
Misspecification and Averaging
57
Homework: The Impact of Model Choice
58
Lab 7: Linear Algebra Perspective
59
Inverse Probability Weighted Least Squares
60
Estimating IPW Weights
61
Meeting 21: Trees
62
Application: Profit vs. Outcomes in Heart Attack Patients
References
References
62
Application: Profit vs. Outcomes in Heart Attack Patients