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Causal Inference with R - Experiments

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Course Description

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).
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  1. 1

    Introduction

    Free

    We introduce how experiments help us find causal connections, and take a quick look at controlled experiments.

    Play Chapter Now
    Course Trailer for Causal Inference with R - Experiments
    50 xp
    Introduction to Experiments
    50 xp
    Rainfall in Sonoma County and eBay sales
    50 xp
    iPhone sales and eBay sales
    50 xp
    A Quick Response
    50 xp
    Controlled Experiments
    50 xp
    Buying a New Phone: Controlled experiments
    50 xp
    Let’s Code: Comparing Ratios
    50 xp
    Buying a New Phone: Comparing Ratios
    100 xp
    Is it a Controlled Experiment?
    50 xp
  2. 2

    Randomized Experiments & Statistical Inference

    Free

    These videos and exercises introduce randomized experiments, and explain why we need statistical inference in experiments.

    Play Chapter Now
    Randomized Experiments
    50 xp
    Buying a New Phone: Experimental design
    50 xp
    Buying a New Phone: Sampling
    50 xp
    Let’s Code: Practice Identifying Imbalance
    50 xp
    Selling Old Phones: Looking for Balance
    100 xp
    Selling Old Phones: Identifying Imbalance
    100 xp
    Statistical Inference
    50 xp
    p Values, Confidence Intervals, and Hypothesis Tests
    50 xp
    Let's Code: Getting to Work with Confidence
    50 xp
    Getting to Work With Confidence: Visualize the Data
    100 xp
    Getting to Work With Confidence: Choosing a Departure Time
    100 xp
    Sample Size
    50 xp
    Statistical Inference Across Disciplines
    50 xp
    Fishing for a Good Experimental Design
    50 xp
    Problems with Convenience Sampling
    50 xp
  3. 3

    Practice with Published Experiments - The Oregon Health Insurance Experiment

    Free

    These videos and exercises offer practice in skills used in analyzing experimental data.

    Play Chapter Now
    The Design of the Oregon Health Experiment
    50 xp
    Reading Average Treatment Effect & Confidence Intervals in a Table: Depression in the Oregon Health Experiment
    50 xp
    Confidence Intervals
    50 xp
    Significance
    50 xp
    Let's Code: Practice Reading Tables
    50 xp
    Oregon Health Experiment Data: Reading Tables for Results
    100 xp
    Oregon Health Experiment Data: Reading Tables for Statistical Significance
    100 xp
    A Note on Heteroskedasticity
    50 xp
    Identifying Heteroskedastic Outcomes
    50 xp
    Reading Average Treatment Effect & Confidence Intervals in a Table: Cholesterol in the Oregon Health Experiment
    50 xp
    Interpreting Effect Sizes
    50 xp
    Let's Code: Practice with OHIE data
    50 xp
    Oregon Health Experiment Data: Data Structures
    100 xp
    Oregon Health Experiment Data: Balance Checks
    100 xp
    Oregon Health Experiment Data: Finding an ATE
    100 xp
    Oregon Health Experiment Data: Finding Another ATE
    100 xp
    Increasing the Auction Prices on eGulf
    100 xp
    Where the Course Will Go From Here
    50 xp
  4. 4

    Common Issues in Experiments

    Free

    These videos and exercises discuss common issues in carrying out experiments.

    Play Chapter Now
    Important Issues in Experiment Design These Modules Ignore
    50 xp
    Other Issues in Experiments (Including Money & Ethics)
    50 xp
    Drawing Conclusions from Experiments
    50 xp
    Generalizing Experimental Results
    50 xp
    Ethics in experiments
    50 xp
    Common Issues in Interpretation I: Box-and-Whisker Plots
    50 xp
    Let's Code: Common Issues II - Non-Normal Distributions
    50 xp
    Common Issues in Interpretation II: Non-Normal Distributions
    100 xp
    Common Issues in Interpretation II: Non-Normal Distributions (2)
    100 xp
    Let's Code: Common Issues III - Outliers
    50 xp
    Common Issues in Interpretation III: Outliers
    100 xp
  5. 5

    Dealing with Noncompliance in Experiments

    Free

    These videos and exercises discuss the issue of noncompliance in experiments.

    Play Chapter Now
    Noncompliance in Experiments
    50 xp
    Ways to Deal with Noncompliance
    50 xp
    A Refresher on eGulf Auctions Experiment and Confidence Intervals
    50 xp
    Let's Code: Noncompliance in eGulf Data
    50 xp
    eGulf Auctions Experiment: Noncompliance
    100 xp
    Survey Noncompliance
    50 xp
    Offering a Higher Credit Card Limit: Quantifying Noncompliance Concerns
    50 xp
    Let's Code: Working with Noncompliance
    50 xp
    Offering a Higher Credit Card Limit: Working with Noncompliance
    100 xp
  6. 6

    Long-term Average Treatment Effects

    Free

    These videos and exercises discuss how to compute and interpret long-term causal effects.

    Play Chapter Now
    Introducing Perry Preschool
    50 xp
    Perry Preschool: Comparing Educational Attainment Data
    50 xp
    Let's Code: Practice Identifying Heterogenous Outcomes
    50 xp
    Unhappiness at Unter: Identifying Heterogeneous Outcomes - ATE
    100 xp
    Unhappiness at Unter: Identifying Heterogeneous Outcomes - CATEs
    50 xp
    Unhappiness at Unter: Identifying Heterogenous Outcomes - CATEs (2)
    100 xp
    Perry Preschool: Calculating the Lifetime Cost of Crime
    50 xp
    Identifying Underlying Mechanisms
    50 xp
    A Bayesian Primer and ATEs
    50 xp
    Finding Lifetime Outcomes of Experiments
    50 xp
    Reasons to Use Proxy Variables
    50 xp
    Choosing a Good Proxy Variable
    50 xp
    Dr. Max Funn: Questionable Proxy Variables
    50 xp
    Let's Code: Practice Identifying Bad Proxy Variables
    50 xp
    Dr. Max Funn: Identifying Spurious Results with Bad Proxy Variables
    100 xp
    Issues with Sample Sizes & Extrapolation
    50 xp
    Reporting Informative Results
    50 xp
    Experiment Design & RCTs
    50 xp
    Levels vs. Percentages in Long-term Average Treatment Effects
    50 xp
    Following Up with Gumville Book Drop Experiment
    50 xp
    A Furry Conclusion to Long-term Average Treatment Effects
    50 xp
    Spillover Effects Among Subjects
    50 xp
  7. 7

    Natural Experiments

    Free

    These videos and exercises introduce natural experiments, sometimes called quasi-experiments.

    Play Chapter Now
    The Two Kinds of Natural Experiments
    50 xp
    As-if Natural experiments
    50 xp
    Public Policy & Other Ways to Find Natural Experiments
    50 xp
    Justifying As-If Randomization
    50 xp
    Let's Code: A Bad Justification for As-If Randomization
    50 xp
    A Bad Argument for As-If Randomization with Katie Perrie
    100 xp
    Analyzing Natural Experiments
    50 xp
    Analyzing Data from Natural Experiments
    50 xp
    Manipulating Confidence Intervals: Practice with Sea Otter Diets
    100 xp
    Squatter Property Rights: Effect on Teenage Pregnancy
    50 xp
    Interpreting the Results of Natural Experiments
    50 xp
    Let's Code: Practice with a Natural Experiment
    50 xp
    Offering a Higher Credit Card Limit: a Natural Experiment
    100 xp
    London Cholera Outbreak: Early Data Visualizations
    50 xp
    London Cholera Outbreak: Was it a Natural Experiment?
    50 xp
  8. 8

    Bounds Analysis & Putting It All Together

    Free

    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.

    Play Chapter Now
    Bounds Analysis for Missing Data
    50 xp
    Let's Code: Computing Bounds Under Noncompliance
    50 xp
    Offering a Higher Credit Card Limit: Computing Bounds Under Noncompliance
    100 xp
    Experimental Design & RCTs
    50 xp
    Splitting Samples, Dimensionality, and Big Data
    50 xp
    Let's Code: Putting it All Together
    50 xp
    Putting it All Together with KittyCatch: Part 1 - Explore the Data
    100 xp
    Putting it All Together with KittyCatch: Part 2 - Use Graphs to Understand the Outcome
    100 xp
    Putting it All Together with KittyCatch: Part 3 - Balance the Data
    100 xp
    Putting it All Together with KittyCatch: Part 4 - Missing Data Problems
    100 xp
    Putting it All Together with KittyCatch: Part 5 - Calculate ATE
    100 xp
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Matt Masten HeadshotMatt Masten

I'm an econometrician working on identification and causal inference.
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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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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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