Causality Inference with Observational Data in Economics

Abstract

Volume 7 • Issue 2 • 1000e106 Bus Eco J ISSN: 2151-6219 BEJ, an open access journal Editorial Economists use non-experimental data for causal inference in most empirical research. The causal effect of a treatment for an individual is the difference between potential outcomes with treatment and without treatment. Angrist and Piscke present a simple example from health insurance in which the treatment group consists of individuals with health insurance, the control group consists of individuals without health insurance and the outcome is the health status. Selection bias is a serious problem in measuring causal effect of a treatment with non-experimental data. However, random assignment of treatment to subjects eliminates the selection bias since it makes the treatment variable independent of potential outcomes. Application of data analysis to answer cause-and-effects questions in economics constitutes the field of applied econometrics. Conclusions derived under ceteris paribus conditions have a causal interpretation. Real-world other things equal comparisons are difficult to accomplish. Applied econometricians use data to achieve other-things-equal in spite of the obstacles, including selection bias or omitted variable bias encountered on the path from raw data to reliable causal knowledge. The path to causal understanding is complicated by selection bias, but applied econometricians employ clever techniques to eliminate or minimize it and link cause and effect. The purpose of this note is to discuss the merits and drawbacks of these techniques, including randomized trials, regression, instrumental variables, regression discontinuity design, and difference in differences. Details can be found in in Angrist and Pischke. Randomized Trials: The gold standard in cause-effect investigations is a randomized experiment, often called a randomized trial. Experimental random assignment is both a framework for causal questions and a benchmark for comparison for other methods of causal inference. The main challenge for applied econometricians is elimination of the selection bias that arises from unobserved differences between treatment and control groups. In a randomized trial, researchers change the variables of interest, such as the availability of college financial aid for a group selected through randomization using something like a coin toss. Changing circumstances randomly makes it highly likely that the variable of interest is unrelated to the many other factors determining the outcomes we are interested in studying. Thus random assignment has the same effect as holding everything else fixed. Unfortunately, randomized social experiments are expensive to conduct and may be slow to yield results. Often, therefore, applied econometricians turn to less powerful but more accessible research designs. Angrist and Pischke show how wise application of some econometric tools brings us as close as possible to the causality-revealing power of a real experiment. We now turn to a discussion of these tools.