Outline

I. Descriptive Inference

1. Introduction
1. Overview and Course Requirements
2. Course Outline
3. Introductory Sampling Activity
2. Descriptive Questions
1. Describing Univariate Populations
2. Describing Bivariate and Multivariate Populations
3. Summarization with Bivariate and Multivariate Regression
3. Randomly Sampled Observations and Basic Probability
1. Elementary Probability Theory
2. Random Variables and Functions of Random Variables (Expectation, Variance, ...)
3. Joint and Conditional Distributions
4. Random Samples and Descriptive Inference (Univariate)
1. Simple Random Sampling (with and without replacement)
2. Distribution of the Sample as an Estimate of the Population Distribution
3. Sample Statistics
4. Sampling Distributions
5. Point Estimation
6. Interval Estimation (i.e., confidence intervals)
7. Hypothesis Testing
5. Random Samples and Descriptive Inference (Regression)
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 and Sampling Distributions
5. Point Estimation and Interval Estimation
6. Hypothesis Testing
6. Diagnosing and/or Fixing Problems (Part 1)
1. Nonlinearity
2. Nonconstant Error Variance and Correlated Errors
3. Weighted Least Squares and Generalized Least Squares
4. "Robust" Standard Errors
5. Nonnormality
6. Unusual Observations (leverage points, outliers, and influence points)
7. Diagnosing and/or Fixing Problems (Part 2)
1. Data Missing at Random (conditional on observed data)
2. Bounding and Sensitivity Analysis for Data Not Missing at Random

II. Causal Inference

1. Introduction
1. Potential Outcomes and Causal Effects
2. Causal Inference as a Missing Data Problem
3. Introductory Causal Inference Activity
2. Causal Questions
1. Describing Univariate Distributions of Potential Outcomes
2. Describing Univariate Distributions of Causal Effects
3. Conditional Distributions of Potential Outcomes and Causal Effects
4. Causal Questions Not Addressed in the Course
3. Randomized Treatment Assignment
1. Identification with Randomized and Conditionally Randomized Treatment Assignment
2. Estimation, CIs, and Testing with Randomized and Conditionally Randomized Treatment Assignment
4. Observational Studies with Measured Confounding
1. The Assumption of No Unmeasured Confounding
2. Relation to Classical Econometric Assumptions
3. Choosing Conditioning Variables
4. Regression Based Estimation (Additive and Interactive)
5. Diagnosing and/or Fixing Problems
1. Assessing Overlap and Balance
2. Revising the Question of Interest
3. Bounding and Sensitivity Analysis for Unmeasured Confounding