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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).
Regressions 1: Introduction to Regression As CausalityFree
This chapter will introduce you to using regression analysis to find causal effectsCourse Trailer for Causal Inference with R - Regression50 xpIntroduction to Regression Analysis50 xpInterpreting Regressions50 xpReversing Causal Direction50 xpLet’s Code: Comparing Hospital Quality to Mortality Rates50 xpComparing Hospital Quality to Mortality Rates100 xpRegression Models and Policy Implementation50 xpBasic Elements of a Regression Table50 xpReading a Bivariate Regression Table50 xpReading a Multivariate (Multiple) Regression Table50 xpThe Relationship between Economic Development and Property Rights50 xpRegressions with small coefficients and small confidence intervals50 xpRecalling terminology50 xpMultiple Regression Models50 xpLet’s Code: Running a Regression Model50 xpRunning a Regression Model: A Simple Beginning100 xpRunning a Regression Model: Improving Our Model100 xpSteps Prior to Analysis with Regression Models50 xp
Regressions 2: Using Regression to Estimate Causal EffectsFree
This chapter will introduce you to using regression analysis to find causal effectsUsing Regression to Get Causal Effects: Unconfoundedness50 xpRegressing Soda pop and K-pop50 xpKnowing it all50 xpThe Uncounfoundedness Assumption50 xpHow to Compute Regressions: Ordinary Least Squares (OLS)50 xpLet’s Code: Toying with OLS - Outliers & Statistical Power50 xpToying with OLS I: Outliers100 xpToying with OLS II: Statistical Power100 xpLet’s Code: Toying with OLS III - Model Selection50 xpToying with OLS III: Model Selection100 xpCommon Statistical Terms and Transformations in Regression Models50 xpIdenfifying Non-Linear Relationships50 xpStatistical Interactions in Regression Models50 xpLet’s Code: Creating a Regression Model with Interaction Effects50 xpCreating a Regression Model With Interaction Effects: Part 1100 xpCreating a Regression Model With Interaction Effects: Part 2, Mediating and Moderating Effects100 xpLogistic Regression Models50 xpWhen to Use a Logistic Regression Model50 xpDefining The Average Effect of Treatment on the Treated50 xpAverage Effect of Treatment on the Treated50 xpHow to Compute ATE Under Unconfoundedness, and What Not to Do50 xpLet’s Code: Practice with Survey Weights50 xpPractice with Survey Weights: Part 1100 xpPractice with Survey Weights: Part 2100 xpWhen Survey Weights Are Unnecessary50 xp
Regressions 3: Introduction to Matching MethodsFree
This chapter will introduce you to using matching methods to find causal effectsMatching Methods50 xpShould Megan Use Matching Methods With Her Survey?50 xpCan Your Survey Design Affect Your Matching Methods?50 xpProblems with Matching Methods When Comparing Individuals?50 xpThe Lifetime Earnings of Veterans and Nonveterans50 xpWhy We Need Matching Methods50 xpThe Unconfoundedness Assumption in Angrist's Study on the Effect of Veteran Status on Lifetime Earnings50 xpReplication and Validity50 xpCausal Inference with Matching Methods50 xpThe Effect of Volunteer Military Service on Lifetime Earnings50 xpLet’s Code: Communication Skills in Video Games50 xpCommunication Skills in Video Games: Do We Need to Use Matching Methods?100 xpCommunication Skills in Video Games: Propensity Score Matching in R100 xpThe Credibility of the Unconfoundedness Assumption50 xpAssumptions and Causality50 xp
Matt MastenSee More
I'm an econometrician working on identification and causal inference.
SSRI @ Duke UniversitySee More
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!
Brian AronsonSee More
Lead Question Developer
Tyler RansomSee More
PhD in Economics
Alexandra CooperSee More
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.
Bentley CoffeySee More
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...