• The Effect: An Introduction to Research Design and Causality Book Club
  • Introduction
    • Book club meetings
    • Pace
    • Introductions
    • git and GitHub
    • Group Question 1
    • Group Question 2
    • Group Question 3
    • git and GitHub Resources
    • Learning objectives
    • Today’s learning objectives
  • 1 Designing Research
    • Research Questions
    • Empirical Research
    • Why Research needs a design
    • Book Goals
    • Discussion/Practicals
    • Meeting Videos
      • Cohort 1
  • 2 Research Questions
    • What is a Research Question?
    • Data Mining vs. Research Q’s
    • Considerations for a good Research Q
    • Discussion/Practicals
    • Meeting Videos
      • Cohort 1
  • 3 Describing Variables
    • 3.1 Overview
    • 3.2 Variable types
    • 3.3 Distribution
    • 3.4 Summarizing the distribution
    • 3.5 Mean, Percentiles, IQR
    • 3.6 Variation
    • 3.7 Skew
    • 3.8 Theoretical Distributions
    • Meeting Videos
      • Cohort 1
  • 4 Describing Relationships
    • 4.1 Relationships
    • 4.2 Conditional distributions
    • 4.3 Conditional means
    • 4.4 Line-fitting/regression
    • 4.5 Conditional Conditional Means (not a typo) AKA using controls
    • Meeting Videos
      • Cohort 1
  • 5 Identification
    • 5.1 The Data Generating Process
      • Introduction
      • Two Parts of DGPs
      • Contrived DGP Example
      • Two core ideas
    • 5.2 Where’s Your Variation?
      • Avocado Prices Overview
      • Example Answers to Above Questions
      • DGP and Isolating Variation in Avocados Example
      • Broad ideas
    • 5.3 Identification
      • Example of Family Dog, Rex, Escaping House
      • Identification Process
    • 5.4 Alcohol and Mortality
      • Overview
      • Study Findings
      • Book Exercise
      • Example Answers
      • Issues with Causal Explanation
      • Controls in Study to Address Alternate Explanations
      • Lingering Issues
    • 5.5 Context and Omniscience
    • Meeting Videos
      • Cohort 1
  • 6 Causal Diagrams
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 7 Drawing Causal Diagrams
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 8 Causal Paths and Closing Back Doors
    • 8.1 Paths
    • 8.2 Finding all paths
    • 8.3 Solution
    • 8.4 Let’s try another one: Wine and Health
    • 8.5 Solution
    • 8.6 Path types
    • 8.7 Closing paths
    • 8.8 Colliders
    • 8.9 Using paths to test the DAG
    • Meeting Videos
      • Cohort 1
  • 9 Finding Front Doors
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 10 Treatment Effects
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 11 Causality with Less Modeling
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 12 Opening the Toolbox
    • 12.1 Methods that we’ll be checking out
    • 12.2 Structure of upcoming chapters
    • Meeting Videos
      • Cohort 1
  • 13 Regression
    • 13.1 Basics
    • 13.2 Error terms
    • 13.3 Regression assumptions
    • 13.4 Sampling variation
    • 13.5 Hypothesis testing in OLS
    • 13.6 Mantras about hypothesis testing
    • 13.7 Regression tables
      • 13.7.1 Interpretation
      • 13.7.2 Controls
    • 13.8 Subscripts in regression equations
    • 13.9 DAG to Regression
    • 13.10 Getting fancier
    • 13.11 Binary/discrete variables
    • 13.12 Polynomials
    • 13.13 Variable transformation
      • 13.13.1 Options
      • 13.13.2 Interpretation of log
    • 13.14 Interaction terms
    • 13.15 Nonlinear regressions
      • 13.15.1 good link functions
      • 13.15.2 Interpretation
    • 13.16 Standard errors
      • 13.16.1 Assumptions
      • 13.16.2 Fixes (mostly sandwich estimators)
      • 13.16.3 Bootstrapping
    • 13.17 Sample Weights
    • 13.18 Collinearity
    • 13.19 Measurement error
    • 13.20 Penalized regression
    • Meeting Videos
      • Cohort 1
  • 14 Matching
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 15 Simulation
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 16 Fixed Effects
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 17 Event Studies
    • 17.1 How does it work?
    • 17.2 Prediction and Deviation
    • 17.3 Terminology
    • 17.4 How it’s used in Finance
    • 17.5 How it’s used with regressions
      • 17.5.1 Example: Improved ambulance care
    • 17.6 How it’s used when taking time series seriously
    • 17.7 Forecasting with Time Series Models
    • 17.8 Joint tests
    • Meeting Videos
      • Cohort 1
  • 18 Difference-in-Differences
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 19 Instrumental Variables
    • 19.1 How does it work?
    • 19.2 Assumptions
    • 19.3 Canonical designs
    • 19.4 Instrumental Variables estimator
    • 19.5 Example: Insurance takeup
      • 19.5.1 2SLS
      • 19.5.2 GMM
    • 19.6 IV and treatment effects
    • 19.7 Checking IV assumptions
    • 19.8 How the Pros do it
    • 19.9 Don’t just test for weakness, fix it
    • 19.10 Way past LATE
    • 19.11 Nonlinear IV
    • 19.12 Validity violation
    • Meeting Videos
      • Cohort 1
  • 20 Regression Discontinuity
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 21 A Gallery of Rogues: Other Methods
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 22 Under the Rug
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • Published with bookdown

The Effect: An Introduction to Research Design and Causality Book Club

git and GitHub Resources

  • Happy Git and GitHub for the useR
  • usethis’s pull request helpers
  • git’s documentation
  • MShiny Cohort 2 Introduction