Regression and Other Stories Book Club
Welcome
1 Introduction
2 Data and measurement
- 2.1 SLIDE 1
- 2.2 Meeting Videos
  - 2.2.1 Cohort 1
  - 2.2.2 Cohort 2
3 Some basic methods in mathematics and probability
- 3.1 SLIDE 1
- 3.2 Meeting Videos
  - 3.2.1 Cohort 1
  - 3.2.2 Cohort 2
4 Statistical inference
5 Simulation
- 5.1 SLIDE 1
- 5.2 Meeting Videos
  - 5.2.1 Cohort 1
  - 5.2.2 Cohort 2
6 Background on regression modeling
- 6.1 SLIDE 1
- 6.2 Meeting Videos
  - 6.2.1 Cohort 1
  - 6.2.2 Cohort 2
7 Linear regression with a single predictor
8 Fitting regression models
- 8.1 SLIDE 1
- 8.2 Meeting Videos
  - 8.2.1 Cohort 1
  - 8.2.2 Cohort 2
9 Prediction and Bayesian inference
- 9.1 SLIDE 1
- 9.2 Meeting Videos
  - 9.2.1 Cohort 1
  - 9.2.2 Cohort 2
10 Linear regression with multiple predictors
- 10.1 SLIDE 1
- 10.2 Meeting Videos
  - 10.2.1 Cohort 1
  - 10.2.2 Cohort 2
11 Assumptions, diagnostics, and model evaluation
12 Transformations
- 12.1 SLIDE 1
- 12.2 Meeting Videos
  - 12.2.1 Cohort 1
  - 12.2.2 Cohort 2
13 Logistic regression
14 Working with logistic regression
- 14.1 SLIDE 1
- 14.2 Meeting Videos
  - 14.2.1 Cohort 2
15 Other generalized linear models
- 15.1 SLIDE 1
- 15.2 Meeting Videos
  - 15.2.1 Cohort 2
16 Design and sample size decisions
17 Poststratification and missing-data imputation
- 17.1 SLIDE 1
- 17.2 Meeting Videos
  - 17.2.1 Cohort 2
18 Causal inference basics and randomized experiments
19 Causal inference using regression on the treatment variable
- 19.1 SLIDE 1
- 19.2 Meeting Videos
  - 19.2.1 Cohort 2
20 Observational studies assuming all confounders measured
- 20.1 SLIDE 1
- 20.2 Meeting Videos
  - 20.2.1 Cohort 2
21 More advanced topics in causal inference
- 21.1 SLIDE 1
- 21.2 Meeting Videos
  - 21.2.1 Cohort 2
22 Advanced regression and multilevel models
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Regression and Other Stories Book Club

Causual Inference

Assumes randomized or balanced samples
With that, we can estimate directly the causal effect from regression analysis