Module 2: Selection Bias & the Experimental Ideal
Overview
This module introduces the potential outcomes framework that we’ll use throughout the course as well as the concept of selection bias before
explaining how average treatment effects can be estimated when treatment status is randomly assigned.
Readings
Running Randomized Evaluations: Chapter 2
Mastering Metrics: Chapter 1
Review Questions
- How do Americans with health insurance differ from those without health insurance? Are those differences likely to represent the causal effects of having health insurance? Why or why not?
- What are potential outcomes? How do potential outcomes lead to a missing data problem in causal inference?
- What is selection bias, and what implications does it have for program evaluation?
- What is the Law of Large Numbers, and why is it important in randomized experiments?
- Based on the evidence presented in the reading, what are the impacts of access to health insurance (in the United States)?
Video Lectures (from 2021)
Lecture 2.1 The Potential Outcomes Framework (26:44)
Review Questions
- What is the fundamental problem of causal inference?
- What are potential outcomes, and how do they create a missing data problem in program evaluation?
- What is selection bias? When and why is it likely to bias estimates of program impacts?
Lecture 2.2 The Experimental Ideal (13:17)
Review Questions
- How can random assignment eliminate selection bias?
- What is the Law of Large Numbers, and why is it important in randomized experiments?
- How can we estimate the causal impacts of a program when treatment assignments are randomized?
Lecture 2.3 A Short History of Randomized Experiments (27:35)
Review Questions
- Who was Ronald Fisher, and how did he contribute to the development of randomized experiments?
- When were randomized trials first used in medicine? When were they first used in the social sciences?
- What were the first randomized evaluations used in the international development context?
Lecture 2.4 Analysis of Randomized Experiments (8:32)
Review Questions
- When treatment is randomly assigned, how can we test the null hypothesis thta the average treatment effect is equal to zero?
- How can linear regression be used to analyze data from randomized experiments?
Lecture Slides (from 2021)
A handout version of the lecture slides is available here.
If you wish to use these slides for teaching, the underlying beamer files and supporting materials are here.
Papers Mentioned in the Lectures
The Design of Experiments, Chapter 2
The Entry of Randomized Assignment into the Social Sciences by Julian Jamison
Do Conditional Cash Transfers Improve Child Health? Evidence from PROGRESA’s Control Randomized Experiment by Paul Gertler
Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities by Edward Miguel and Michael Kremer
Empirical Exercise
Empirical Exercise 2, Part 1 is here.
Further Reading
More Recent Evidence on Health Insurance
Health Insurance and Mortality: Experimental Evidence from Taxpayer Outreach
On the “Credibility” Revolution in Empirical Microeconomics
The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics by Joshua D. Angrist and Jörn-Steffen Pischke
Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009) by Guido Imbens