Become skeptical about claims of cause and effect, realize when there are flaws in others’ causal claims, and communicate reasons to be doubtful.

Understand why experimentation is the best method for solving the causal-inference problem, why randomization is key to experimentation, and when “natural” or quasi-experiments are a good substitute.

Be able to describe examples of successful experiments, why we feel confident in the results, and the limits to what we learn from any experiment.

Understand statistical concepts more intuitively and how to quantify uncertainty using standard errors, confidence intervals, and statistical power calculations.

Be able to analyze experimental data and interpret results, including how to address common issues that arise in an experimental analysis, such as non-compliance, placebo effects, and sample attrition.

Design, pilot, and pitch an experimental analysis to a firm or organization.

Introduction to experimental methods and design. Econometric Analysis of Experiments: estimating average & heterogeneous treatment effects. Non-compliance and sample attrition. Quasi-Experimental Analysis: Regression discontinuity and differences in differences.

-MEAN

-Students should understand how to run and interpret a linear regression, including the concepts of standard errors, P-values, and confidence intervals.

JONAH ROCKOFF.  PhD. in Economics from Harvard University and B.A. in Economics from Amherst College. Jonah is a professor of Business at the Columbia Graduate School of Business and a Research Associate at the National Bureau of Economic Research. Professor Rockoff’s interests center on the finance and management of public schools. His most recent research focuses on systems for hiring new teachers, the effects of No Child Left Behind on students and schools, the impact of removing school desegregation orders, and how primary school teachers affect students’ outcomes in early adulthood.