Welcome to the Causal Inference with R – Experiments, the 2nd of 7 courses on causal inference concepts and methods created by Duke University with support from eBay, Inc. Designed to teach you about concepts, methods, and how to code in R with realistic data, this course focuses on experiment design, working with data from controlled and natural experiments, and dealing with noncompliance. 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 experimental data. The course instructors and creators are Dr. Matt Masten (Duke University), James Speckart (Duke), Brian Aronson (Duke), Dr. Tyler Ransom (University of Oklahoma), Dr. Bentley Coffey (University of South Carolina), and Alexandra Cooper (Duke).
We introduce how experiments help us find causal connections, and take a quick look at controlled experiments.Course Trailer for Causal Inference with R - Experiments50 xpIntroduction to Experiments50 xpRainfall in Sonoma County and eBay sales50 xpiPhone sales and eBay sales50 xpA Quick Response50 xpControlled Experiments50 xpBuying a New Phone: Controlled experiments50 xpLet’s Code: Comparing Ratios50 xpBuying a New Phone: Comparing Ratios100 xpIs it a Controlled Experiment?50 xp
Randomized Experiments & Statistical InferenceFree
These videos and exercises introduce randomized experiments, and explain why we need statistical inference in experiments.Randomized Experiments50 xpBuying a New Phone: Experimental design50 xpBuying a New Phone: Sampling50 xpLet’s Code: Practice Identifying Imbalance50 xpSelling Old Phones: Looking for Balance100 xpSelling Old Phones: Identifying Imbalance100 xpStatistical Inference50 xpp Values, Confidence Intervals, and Hypothesis Tests50 xpLet's Code: Getting to Work with Confidence50 xpGetting to Work With Confidence: Visualize the Data100 xpGetting to Work With Confidence: Choosing a Departure Time100 xpSample Size50 xpStatistical Inference Across Disciplines50 xpFishing for a Good Experimental Design50 xpProblems with Convenience Sampling50 xp
Practice with Published Experiments - The Oregon Health Insurance ExperimentFree
These videos and exercises offer practice in skills used in analyzing experimental data.The Design of the Oregon Health Experiment50 xpReading Average Treatment Effect & Confidence Intervals in a Table: Depression in the Oregon Health Experiment50 xpConfidence Intervals50 xpSignificance50 xpLet's Code: Practice Reading Tables50 xpOregon Health Experiment Data: Reading Tables for Results100 xpOregon Health Experiment Data: Reading Tables for Statistical Significance100 xpA Note on Heteroskedasticity50 xpIdentifying Heteroskedastic Outcomes50 xpReading Average Treatment Effect & Confidence Intervals in a Table: Cholesterol in the Oregon Health Experiment50 xpInterpreting Effect Sizes50 xpLet's Code: Practice with OHIE data50 xpOregon Health Experiment Data: Data Structures100 xpOregon Health Experiment Data: Balance Checks100 xpOregon Health Experiment Data: Finding an ATE100 xpOregon Health Experiment Data: Finding Another ATE100 xpIncreasing the Auction Prices on eGulf100 xpWhere the Course Will Go From Here50 xp
Common Issues in ExperimentsFree
These videos and exercises discuss common issues in carrying out experiments.Important Issues in Experiment Design These Modules Ignore50 xpOther Issues in Experiments (Including Money & Ethics)50 xpDrawing Conclusions from Experiments50 xpGeneralizing Experimental Results50 xpEthics in experiments50 xpCommon Issues in Interpretation I: Box-and-Whisker Plots50 xpLet's Code: Common Issues II - Non-Normal Distributions50 xpCommon Issues in Interpretation II: Non-Normal Distributions100 xpCommon Issues in Interpretation II: Non-Normal Distributions (2)100 xpLet's Code: Common Issues III - Outliers50 xpCommon Issues in Interpretation III: Outliers100 xp
Dealing with Noncompliance in ExperimentsFree
These videos and exercises discuss the issue of noncompliance in experiments.Noncompliance in Experiments50 xpWays to Deal with Noncompliance50 xpA Refresher on eGulf Auctions Experiment and Confidence Intervals50 xpLet's Code: Noncompliance in eGulf Data50 xpeGulf Auctions Experiment: Noncompliance100 xpSurvey Noncompliance50 xpOffering a Higher Credit Card Limit: Quantifying Noncompliance Concerns50 xpLet's Code: Working with Noncompliance50 xpOffering a Higher Credit Card Limit: Working with Noncompliance100 xp
Long-term Average Treatment EffectsFree
These videos and exercises discuss how to compute and interpret long-term causal effects.Introducing Perry Preschool50 xpPerry Preschool: Comparing Educational Attainment Data50 xpLet's Code: Practice Identifying Heterogenous Outcomes50 xpUnhappiness at Unter: Identifying Heterogeneous Outcomes - ATE100 xpUnhappiness at Unter: Identifying Heterogeneous Outcomes - CATEs50 xpUnhappiness at Unter: Identifying Heterogenous Outcomes - CATEs (2)100 xpPerry Preschool: Calculating the Lifetime Cost of Crime50 xpIdentifying Underlying Mechanisms50 xpA Bayesian Primer and ATEs50 xpFinding Lifetime Outcomes of Experiments50 xpReasons to Use Proxy Variables50 xpChoosing a Good Proxy Variable50 xpDr. Max Funn: Questionable Proxy Variables50 xpLet's Code: Practice Identifying Bad Proxy Variables50 xpDr. Max Funn: Identifying Spurious Results with Bad Proxy Variables100 xpIssues with Sample Sizes & Extrapolation50 xpReporting Informative Results50 xpExperiment Design & RCTs50 xpLevels vs. Percentages in Long-term Average Treatment Effects50 xpFollowing Up with Gumville Book Drop Experiment50 xpA Furry Conclusion to Long-term Average Treatment Effects50 xpSpillover Effects Among Subjects50 xp
These videos and exercises introduce natural experiments, sometimes called quasi-experiments.The Two Kinds of Natural Experiments50 xpAs-if Natural experiments50 xpPublic Policy & Other Ways to Find Natural Experiments50 xpJustifying As-If Randomization50 xpLet's Code: A Bad Justification for As-If Randomization50 xpA Bad Argument for As-If Randomization with Katie Perrie100 xpAnalyzing Natural Experiments50 xpAnalyzing Data from Natural Experiments50 xpManipulating Confidence Intervals: Practice with Sea Otter Diets100 xpSquatter Property Rights: Effect on Teenage Pregnancy50 xpInterpreting the Results of Natural Experiments50 xpLet's Code: Practice with a Natural Experiment50 xpOffering a Higher Credit Card Limit: a Natural Experiment100 xpLondon Cholera Outbreak: Early Data Visualizations50 xpLondon Cholera Outbreak: Was it a Natural Experiment?50 xp
Bounds Analysis & Putting It All TogetherFree
This chapter discusses how to form bounds on causal effects in the presence of experimental noncompliance, and we wrap up the course with a bigger exercise to review what we have learned.Bounds Analysis for Missing Data50 xpLet's Code: Computing Bounds Under Noncompliance50 xpOffering a Higher Credit Card Limit: Computing Bounds Under Noncompliance100 xpExperimental Design & RCTs50 xpSplitting Samples, Dimensionality, and Big Data50 xpLet's Code: Putting it All Together50 xpPutting it All Together with KittyCatch: Part 1 - Explore the Data100 xpPutting it All Together with KittyCatch: Part 2 - Use Graphs to Understand the Outcome100 xpPutting it All Together with KittyCatch: Part 3 - Balance the Data100 xpPutting it All Together with KittyCatch: Part 4 - Missing Data Problems100 xpPutting it All Together with KittyCatch: Part 5 - Calculate ATE100 xp
Matt MastenSee More
I'm an econometrician working on identification and causal inference.
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...
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.