Lecture 1: Introduction ECON 335 Simon Woodcock Spring 2024 What this course is about • This is a course about Causal Inference. • Causal Inference refers to the statistical/econometric tools that we use to learn about Cause and Effect in the world around us. • Policy Evaluation is one of the main venues in which causal inference is applied, and these terms are more or less synonymous for economists. • We will develop a framework for thinking formally and precisely about cause and effect in economic/social data. • You will learn a set of econometric tools to estimate causal effects. • You will learn how to apply these tools in R. Correlation and Causation • You probably remember being told in ECON 333 or some other course that correlation does not imply causation. • That is, just because we observe two variables “moving together” in the real world doesn’t mean that one causes the other. • e.g., cities that have more police officers per capita also tend to have higher crime rates. Do police officers cause crime? • e.g., hospitals are full of sick people. Do hospitals make people sick? • e.g., more educated people earn more, on average, than less educated people. Does education cause higher wages? • e.g., spot prices for electricity are higher on very hot and very cold days. Does weather affect electricity prices? Cause and Effect • But often, we really want to know whether one thing (𝑋) causes another (𝑌). And if so, what are the sign & magnitude of the causal effect of 𝑋 on 𝑌? • e.g., Did COVID-19 lockdowns cause people to lose their jobs? How many? And did they save lives? How many? • e.g., consider an aid program that provides free food to families with children in a developing country. How does this affect infant mortality? Family size? Academic achievement? Female labor supply? Tax revenue? • e.g., consider a new carbon tax. How does this affect prices? GDP? Unemployment? • e.g., consider a firm that implements a new marketing program. How does this affect sales? Customer engagement? Brand perception and market share? • e.g., does completing a university degree increase the amount that a person can expect to earn? By how much? • These are all causal questions. Answering causal questions requires a specific toolkit. Tools to answer causal questions • First, we will need a formal framework to think about cause and effect. • This will give us a precise idea of exactly what we’re trying to measure, what could go wrong, and how we might overcome problems that we encounter. • But the main thing you will learn in this course are tools to answer causal questions. • These are primarily statistical/econometric tools. • Most of them rely on regression. • Regression is taught in ECON 333, but we’ll do a quick refresher/introduction here. • And you’ll learn how to apply these tools in R. Why R? • R bridges disciplines: it is used by economists, statisticians, and people doing empirical work in many other fields. • R is used in industry and elsewhere in the “real” world, so it’s a useful skill. • R is taught in most (all?) sections of ECON 333. • R is free. • Once you learn to program in one statistical language, it’s easy to learn another. This course vs. ECON 334 • This course is primarily focused on questions of cause and effect, and how to answer them. We’ll use R as a tool to do that. • 334 is more focused on developing proficiency in R, data visualization, and exploratory analysis. It is less focused on the econometrics, and doesn’t consider causal questions. • Both are useful! Approximate plan for the semester • Quick review of statistical/econometric methods and (re?)-introduction to R • Cause and Effect (Chapter 1) • Potential outcomes model • Treatment effects • Selection bias • Randomized Experiments (Chapter 1) • Regression (Chapter 2) • Intro/review • Apples-to-apples comparisons and the CEF • Omitted variables bias vs. selection bias • Other considerations: standard errors & robustness checks • Instrumental Variables (Chapter 3) • Regression Discontinuity Design (Chapter 4) • Difference-in-Differences, Triple Differences, and Fixed Effects (Chapter 5)
