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Causal Inference with R - Regression

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Course Description

Welcome to the 3rd course in our series on causal inference concepts and methods created by Duke University with support from eBay, Inc. Designed to teach you causal inference concepts, methods, and how to code in R with realistic data, this course focuses on how to use regression to find causal effects, why they can be controversial, and what they look like in practice. We’ll stay away from dense statistical math and focus instead on higher level concepts that data scientists need to always consider when examining and making inferences about data. The course instructors and creators are Dr. Matt Masten (Duke University), James Speckart (Duke), Brian Aronson (Duke), Dr. Tyler Ransom (University of Oklahoma), and Alexandra Cooper (Duke).
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  1. 1

    Regressions 1: Introduction to Regression As Causality

    Free

    This chapter will introduce you to using regression analysis to find causal effects

    Play Chapter Now
    Course Trailer for Causal Inference with R - Regression
    50 xp
    Introduction to Regression Analysis
    50 xp
    Interpreting Regressions
    50 xp
    Reversing Causal Direction
    50 xp
    Let’s Code: Comparing Hospital Quality to Mortality Rates
    50 xp
    Comparing Hospital Quality to Mortality Rates
    100 xp
    Regression Models and Policy Implementation
    50 xp
    Basic Elements of a Regression Table
    50 xp
    Reading a Bivariate Regression Table
    50 xp
    Reading a Multivariate (Multiple) Regression Table
    50 xp
    The Relationship between Economic Development and Property Rights
    50 xp
    Regressions with small coefficients and small confidence intervals
    50 xp
    Recalling terminology
    50 xp
    Multiple Regression Models
    50 xp
    Let’s Code: Running a Regression Model
    50 xp
    Running a Regression Model: A Simple Beginning
    100 xp
    Running a Regression Model: Improving Our Model
    100 xp
    Steps Prior to Analysis with Regression Models
    50 xp
  2. 2

    Regressions 2: Using Regression to Estimate Causal Effects

    Free

    This chapter will introduce you to using regression analysis to find causal effects

    Play Chapter Now
    Using Regression to Get Causal Effects: Unconfoundedness
    50 xp
    Regressing Soda pop and K-pop
    50 xp
    Knowing it all
    50 xp
    The Uncounfoundedness Assumption
    50 xp
    How to Compute Regressions: Ordinary Least Squares (OLS)
    50 xp
    Let’s Code: Toying with OLS - Outliers & Statistical Power
    50 xp
    Toying with OLS I: Outliers
    100 xp
    Toying with OLS II: Statistical Power
    100 xp
    Let’s Code: Toying with OLS III - Model Selection
    50 xp
    Toying with OLS III: Model Selection
    100 xp
    Common Statistical Terms and Transformations in Regression Models
    50 xp
    Idenfifying Non-Linear Relationships
    50 xp
    Statistical Interactions in Regression Models
    50 xp
    Let’s Code: Creating a Regression Model with Interaction Effects
    50 xp
    Creating a Regression Model With Interaction Effects: Part 1
    100 xp
    Creating a Regression Model With Interaction Effects: Part 2, Mediating and Moderating Effects
    100 xp
    Logistic Regression Models
    50 xp
    When to Use a Logistic Regression Model
    50 xp
    Defining The Average Effect of Treatment on the Treated
    50 xp
    Average Effect of Treatment on the Treated
    50 xp
    How to Compute ATE Under Unconfoundedness, and What Not to Do
    50 xp
    Let’s Code: Practice with Survey Weights
    50 xp
    Practice with Survey Weights: Part 1
    100 xp
    Practice with Survey Weights: Part 2
    100 xp
    When Survey Weights Are Unnecessary
    50 xp
  3. 3

    Regressions 3: Introduction to Matching Methods

    Free

    This chapter will introduce you to using matching methods to find causal effects

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    Matching Methods
    50 xp
    Should Megan Use Matching Methods With Her Survey?
    50 xp
    Can Your Survey Design Affect Your Matching Methods?
    50 xp
    Problems with Matching Methods When Comparing Individuals?
    50 xp
    The Lifetime Earnings of Veterans and Nonveterans
    50 xp
    Why We Need Matching Methods
    50 xp
    The Unconfoundedness Assumption in Angrist's Study on the Effect of Veteran Status on Lifetime Earnings
    50 xp
    Replication and Validity
    50 xp
    Causal Inference with Matching Methods
    50 xp
    The Effect of Volunteer Military Service on Lifetime Earnings
    50 xp
    Let’s Code: Communication Skills in Video Games
    50 xp
    Communication Skills in Video Games: Do We Need to Use Matching Methods?
    100 xp
    Communication Skills in Video Games: Propensity Score Matching in R
    100 xp
    The Credibility of the Unconfoundedness Assumption
    50 xp
    Assumptions and Causality
    50 xp
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Matt Masten HeadshotMatt Masten

I'm an econometrician working on identification and causal inference.
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SSRI @ Duke University HeadshotSSRI @ Duke University

We create modular online educational content for Duke University's Social Science Research Institute to share with students and professionals around the world. If you have any feedback for us about any part of this course, please let us know!
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Alexandra Cooper HeadshotAlexandra Cooper

Alexandra Cooper earned a Ph.D. in political science from the University of North Carolina at Chapel Hill and has taught at Duke, UNC, UNC-Charlotte, and most recently, at Lafayette College, before joining Duke University to plan and develop the Social Science Research Institute (SSRI). Initially responsible for SSRI's day-to-day programming and operations, as the institute has grown and added staff, she has had the opportunity to focus on coordinating educational programming. Her key responsibilities include organizing workshops and events, and supporting programming focused on improving knowledge and applications of the tools and methods of the social and behavioral sciences.
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Bentley Coffey HeadshotBentley Coffey

I have an unquenchable curiosity and a passion for policy, with an emphasis on the environment. I have studied an unusually broad array of subjects, with a tendency for my analysis to go deep into rigorous modeling and statistics. Yet, my style is casual and flexible...
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