# Introduction

Topics Covered:

1. Overview and Course Requirements
2. Course Outline
3. Introductory Sampling Activity

# Descriptive Questions

Topics Covered:

1. Describing Univariate Populations
2. Describing Bivariate and Multivariate Populations
3. Summarization with Bivariate and Multivariate Regression

# Randomly Sampled Observations and Basic Probability

Topics Covered:

1. Elementary Probability Theory
2. Random Variables and Functions of Random Variables (Expectation, Variance, ...)
3. Joint and Conditional Distributions

# Random Samples and Descriptive Inference

Topics Covered:

1. Simple Random Sampling (with and without replacement)
2. Stratified Random Sampling (with and without replacement)
3. Distribution of the Sample as an Estimate of the Population Distribution
4. Sample Statistics
5. Sampling Distributions (for means and regression parameters)
6. Point Estimation (properties of estimators)
7. Interval Estimation
8. Hypothesis Testing

# Diagnosing and/or Fixing Problems

Topics Covered:

1. Multivariable Regression
2. Nonlinearity
3. Nonconstant Error Variance and Correlated Errors
4. Weighted Least Squares and Generalized Least Squares
5. "Robust" Standard Errors
6. Nonnormality
7. Unusual Observations (leverage points, outliers, and influence points)
8. Data Missing Completely at Random
9. Data Missing at Random (conditional on observables)
10. Simple Approaches to Bounding and Sensitivity Analysis for Nonignorable Missingness
Lecture 7:

Section 7:

Lecture 8:

Section 8:

# Introduction / Causal Questions

Topics Covered:

1. Potential Outcomes and Causal Effects
2. Causal Inference as a Missing Data Problem
3. Introductory Causal Inference Activity
4. Describing Univariate Distributions of Potential Outcomes
5. Describing Univariate Distributions of Causal Effects
6. Conditional Distributions of Potential Outcomes and Causal Effects
7. Causal Questions Not Addressed in the Course

Lecture 9:
Lecture video (2 hrs)
Lecture slides

Section 9 (both Stata and R):
Section video (1 hr)

# Randomized Treatment Assignment

Topics Covered:

1. Identification with Randomized and Conditionally Randomized Treatment Assignment
2. Estimation, CIs, and Testing with Randomized and Conditionally Randomized Treatment Assignment
3. Random Variables and Functions of Random Variables (Expectation, Variance, ...)
4. Joint and Conditional Distributions

Section (for both R and Stata):
Section video (1 hr)

# Observational Studies with Measured Confounding

Topics Covered:

1. The Assumption of No Unmeasured Confounding
2. Relation to Classical Econometric Assumptions
3. Choosing Conditioning Variables
4. Regression Based Estimation (Additive and Interactive)

# Diagnosing and/or Fixing Problems

Topics Covered:

1. Assessing Overlap and Balance
2. Revising the Question of Interest
3. Simple Approaches to Bounding and Sensitivity Analysis for Unmeasured Confounding

# Review

Lecture 13: Recap of Course and Review of Descriptive Inference
Lecture video (2 hrs)

Lecture 14: Review of Causal Inference
Lecture video (2 hrs)
Slides for IV Lecture (Optional)