Econometrics is an application of statistical methods to economic data in order to give empirical content to economic relationships. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference." An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships." Jan Tinbergen is one of the two founding fathers of econometrics. The other, Ragnar Frisch, also coined the term in the sense in which it is used today.
Epidemiology is the study and analysis of the distribution, patterns and determinants of health and disease conditions in a defined population.
John Snow was an English physician and a leader in the development of anaesthesia and medical hygiene. He is considered one of the founders of modern epidemiology and early germ theory, in part because of his work in tracing the source of a cholera outbreak in London's Soho, which he identified as a particular public water pump. Snow's findings inspired fundamental changes in the water and waste systems of London, which led to similar changes in other cities, and a significant improvement in general public health around the world.
Field experiments are experiments carried out outside of laboratory settings.
Health geography is the application of geographical information, perspectives, and methods to the study of health, disease, and health care. Medical geography, a sub-discipline of, or sister field of health geography, focuses on understanding spatial patterns of health and disease in relation to the natural and social environment. Conventionally, there are two primary areas of research within medical geography: the first deals with the spatial distribution and determinants of morbidity and mortality, while the second deals with health planning, help-seeking behavior, and the provision of health services.
The Rubin causal model (RCM), also known as the Neyman–Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes, named after Donald Rubin. The name "Rubin causal model" was first coined by Paul W. Holland. The potential outcomes framework was first proposed by Jerzy Neyman in his 1923 Master's thesis, though he discussed it only in the context of completely randomized experiments. Rubin extended it into a general framework for thinking about causation in both observational and experimental studies.
In epidemiology, Mendelian randomization is a method using measured variation in genes to examine the causal effect of an exposure on an outcome. Under key assumptions, the design reduces both reverse causation and confounding, which often substantially impede or mislead the interpretation of results from epidemiological studies.
Environmental epidemiology is a branch of epidemiology concerned with determining how environmental exposures impact human health. This field seeks to understand how various external risk factors may predispose to or protect against disease, illness, injury, developmental abnormalities, or death. These factors may be naturally occurring or may be introduced into environments where people live, work, and play.
Joshua David Angrist is an Israeli–American economist and Ford Professor of Economics at the Massachusetts Institute of Technology. Angrist, together with Guido Imbens, was awarded the Nobel Memorial Prize in Economics in 2021 "for their methodological contributions to the analysis of causal relationships".
The third cholera pandemic (1846–1860) was the third major outbreak of cholera originating in India in the 19th century that reached far beyond its borders, which researchers at University of California, Los Angeles (UCLA) believe may have started as early as 1837 and lasted until 1863. In the Russian Empire, more than one million people died of cholera. In 1853–1854, the epidemic in London claimed over 10,000 lives, and there were 23,000 deaths for all of Great Britain. This pandemic was considered to have the highest fatalities of the 19th-century epidemics.
The Broad Street cholera outbreak was a severe outbreak of cholera that occurred in 1854 near Broad Street in Soho, London, England, and occurred during the 1846–1860 cholera pandemic happening worldwide. This outbreak, which killed 616 people, is best known for the physician John Snow's study of its causes and his hypothesis that germ-contaminated water was the source of cholera, rather than particles in the air. This discovery came to influence public health and the construction of improved sanitation facilities beginning in the mid-19th century. Later, the term "focus of infection" started to be used to describe sites, such as the Broad Street pump, in which conditions are favourable for transmission of an infection. Snow's endeavour to find the cause of the transmission of cholera caused him to unknowingly create a double-blind experiment.
In epidemiology, ecological studies are used to understand the relationship between outcome and exposure at a population level, where 'population' represents a group of individuals with a shared characteristic such as geography, ethnicity, socio-economic status of employment. What differentiates ecological studies from other studies is that the unit analysis being studied is the group, therefore inferences cannot be made about individual study participants. On the other hand, details of outcome and exposure can be generalized to the population being studied. Examples of such studies include investigating associations between units of grouped data, such as electoral wards, regions, or even whole countries.
Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment. The goal of matching is to reduce bias for the estimated treatment effect in an observational-data study, by finding, for every treated unit, one non-treated unit(s) with similar observable characteristics against which the covariates are balanced out. By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment reducing bias due to confounding. Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods. A simple, easy-to-understand, and statistically powerful method of matching known as Coarsened Exact Matching or CEM.
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The study of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.
Causation in economics has a long history with Adam Smith explicitly acknowledging its importance via his (1776) An Inquiry into the Nature and Causes of the Wealth of Nations and David Hume and John Stuart Mill (1848) both offering important contributions with more philosophical discussions. Hoover (2006) suggests that a useful way of classifying approaches to causation in economics might be to distinguish between approaches that emphasize structure and those that emphasize process and to add to this a distinction between approaches that adopt a priori reasoning and those that seek to infer causation from the evidence provided by data. He represented by this little table which useful identifies key works in each of the four categories.
Guido Wilhelmus Imbens is a Dutch-American economist whose research concerns econometrics and statistics. He holds the Applied Econometrics Professorship in Economics at the Stanford Graduate School of Business at Stanford University, where he has taught since 2012.
The experimentalist approach to econometrics is a way of doing econometrics that, according to Angrist and Krueger (1999): … puts front and center the problem of identifying causal effects from specific events or situations. These events or situations are thought of as natural experiments that generate exogenous variations in variables that would otherwise be endogenous in the behavioral relationship of interest. An example from the economic study of education can be used to illustrate the approach. Here we might be interested in the effect of effect of an additional year of education on earnings. Those working with an experimentalist approach to econometrics would argue that such a question is problematic to answer because, and this is using their terminology, education is not randomly assigned. That is those with different education levels would tend to also have different levels of other variables. And these other variable, many of which would be unobserved, also affect earnings. This renders the causal effect of extra years of schooling difficult to identify. The experimentalist approach looks for an instrumental variable that is correlated with X but uncorrelated with the unobservables.
Victor Chaim Lavy is an Israeli economist and professor at the University of Warwick and the Hebrew University of Jerusalem. His research interests include labour economics, the economics of education, and development economics. Lavy belongs to the most prominent education economists in the world.
In econometrics and related empirical fields, the local average treatment effect (LATE), also known as the complier average causal effect (CACE), is the effect of a treatment for subjects who comply with the experimental treatment assigned to their sample group. It is not to be confused with the average treatment effect (ATE), which includes compliers and non-compliers together. Compliance refers to the human-subject response to a proposed experimental treatment condition. Similar to the ATE, the LATE is calculated but does not include non-compliant parties. If the goal is to evaluate the effect of a treatment in ideal, compliant subjects, the LATE value will give a more precise estimate. However, it may lack external validity by ignoring the effect of non-compliance that is likely to occur in the real-world deployment of a treatment method. The LATE can be estimated by a ratio of the estimated intent-to-treat effect and the estimated proportion of compliers, or alternatively through an instrumental variable estimator.
The 2021 Nobel Memorial Prize in Economic Sciences was divided one half awarded to the American-Canadian David Card "for his empirical contributions to labour economics", the other half jointly to Israeli-American Joshua Angrist and Dutch-American Guido W. Imbens "for their methodological contributions to the analysis of causal relationships." The Nobel Committee stated their reason behind the decision, saying:
"This year's Laureates – David Card, Joshua Angrist and Guido Imbens – have shown that natural experiments can be used to answer central questions for society, such as how minimum wages and immigration affect the labour market. They have also clarified exactly which conclusions about cause and effect can be drawn using this research approach. Together, they have revolutionised empirical research in the economic sciences."
