Linear Models
Prediction, Inference, and Causality
1
About This Book
One-Sample Problems
2
Sampling
3
Homework: Review
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
Probability Review
10
Normal Approximation and Sample Size Calculation
11
Homework: Biased Estimators
12
Bias and Coverage
13
Homework: Comparing Estimators
14
Why the CLT Works: Stein’s Method
15
Practice Midterm 1
16
Sample Size Calculations (Enrichment)
17
Enrichment: Probability Tools
Two-Sample Problems
18
Comparing Two Groups
19
Homework: Variance of Comparisons
20
Randomized Experiments
21
Homework: Randomized Experiments
22
Randomization and Sampling
23
Why Do Models Work?
24
Practice Midterm 2
25
Causality Questions for Unit 2 Exam
Regression Analysis
26
Summarizing Trends involving Many Groups
27
Multivariate Analysis and Adjustment
28
Homework: Covariate Shift
29
A Game of Telephone
30
Inference for Adjusted Comparisons
31
Homework: Point Estimation for Complex Summaries
32
Homework: Calibration for Complex Summaries
33
Conditional Randomization
34
Inverse Probability Weighting
35
Practice Midterm 3
Linear Models
36
Least Squares Regression in Linear Models
37
Least Squares in
R
38
Homework: Simulating Experiments and Identifying the ATE
39
The Behavior of Least Squares Predictors
40
Misspecification and Inference
41
Misspecification and Averaging
42
Homework: IPW and Estimating Treatment Effects
43
Inverse Probability Weighted Least Squares
44
Application: Profit vs. Outcomes in Heart Attack Patients
45
Homework: Misspecification and Bias
46
Practice Final
References
Linear Models
35
Practice Midterm 3
36
Least Squares Regression in Linear Models