Linear Regression with Arbitrary Models
Prediction, Inference, and Causality
1
Introduction
One and Two-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
Normal Approximation and Sample Size Calculation
10
Probability Review: Conditional Expectations
11
Homework: Comparing Estimators
12
Comparing Two Groups
13
Practice Midterm 1
14
Homework: Loose Ends
Linear Regression with Saturated Models
15
Summarizing Trends involving Many Groups
16
Multivariate Analysis and Adjustment
17
Homework: Covariate Shift
18
A Game of Telephone
19
Inference for Complicated Summaries
20
Homework: Calibration for Complex Summaries
21
Potential Outcomes and Causality
22
Practice Midterm 2
Linear Regression with Arbitrary Models
23
Least Squares Regression in Linear Models
24
Least Squares in
R
25
The Behavior of Least Squares Predictors
26
Misspecification and Inference
27
Misspecification and Averaging
28
Inverse Probability Weighting
29
Application: Profit vs. Outcomes in Heart Attack Patients
30
Practice Final
References
Linear Regression with Arbitrary Models
22
Practice Midterm 2
23
Least Squares Regression in Linear Models