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Field experiments are experiments carried out outside of laboratory settings.
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John August List is an American economist known for establishing field experiments as a tool in empirical economic analysis. He works at the University of Chicago, where he serves as Kenneth C. Griffin Distinguished Service Professor; from 2012 until 2018, he served as Chairman of the Department of Economics. Since 2016, he has served as Visiting Robert F. Hartsook Chair in Fundraising at Indiana University Lilly Family School of Philanthropy. List is noted for his pioneering contributions to field experiments in economics, with Nobel prize winning economist George Akerlof and noted law professor Cass Sunstein writing that "List has done more than anyone else to advance the methods and practice of field experiments." Nobel prize winning economist Gary Becker quipped that "John List's work in field experiments is revolutionary."
In fields such as epidemiology, social sciences, psychology and statistics, an observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher because of ethical concerns or logistical constraints. One common observational study is about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator. This is in contrast with experiments, such as randomized controlled trials, where each subject is randomly assigned to a treated group or a control group. Observational studies, for lacking an assignment mechanism, naturally present difficulties for inferential analysis.
A quasi-experiment is an empirical interventional study used to estimate the causal impact of an intervention on target population without random assignment. Quasi-experimental research shares similarities with the traditional experimental design or randomized controlled trial, but it specifically lacks the element of random assignment to treatment or control. Instead, quasi-experimental designs typically allow the researcher to control the assignment to the treatment condition, but using some criterion other than random assignment.
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Impact evaluation assesses the changes that can be attributed to a particular intervention, such as a project, program or policy, both the intended ones, as well as ideally the unintended ones. In contrast to outcome monitoring, which examines whether targets have been achieved, impact evaluation is structured to answer the question: how would outcomes such as participants' well-being have changed if the intervention had not been undertaken? This involves counterfactual analysis, that is, "a comparison between what actually happened and what would have happened in the absence of the intervention." Impact evaluations seek to answer cause-and-effect questions. In other words, they look for the changes in outcome that are directly attributable to a program.
In the statistical analysis of observational data, propensity score matching (PSM) is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. PSM attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect obtained from simply comparing outcomes among units that received the treatment versus those that did not. Paul R. Rosenbaum and Donald Rubin introduced the technique in 1983.
Alvin Eliot Roth is an American academic. He is the Craig and Susan McCaw professor of economics at Stanford University and the Gund professor of economics and business administration emeritus at Harvard University. He was President of the American Economic Association in 2017.
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.
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Sadek Wahba is an American economist and businessman of Egyptian origin. He is the founder and managing partner of the Miami-based global infrastructure investment company I Squared Capital. In 2022 he was appointed by President Biden to the President's National Infrastructure Advisory Council.
Paul R. Rosenbaum is the Robert G. Putzel Professor Emeritus in the Department of Statistics and Data Science at Wharton School of the University of Pennsylvania, where he worked from 1986 through 2021. He has written extensively about causal inference in observational studies, including sensitivity analysis, optimal matching, design sensitivity, evidence factors, quasi-experimental devices, and the propensity score. With various coauthors, he has also written about health outcomes, racial disparities in health outcomes, instrumental variables, psychometrics and experimental design.
Experimental benchmarking allows researchers to learn about the accuracy of non-experimental research designs. Specifically, one can compare observational results to experimental findings to calibrate bias. Under ordinary conditions, carrying out an experiment gives the researchers an unbiased estimate of their parameter of interest. This estimate can then be compared to the findings of observational research. Note that benchmarking is an attempt to calibrate non-statistical uncertainty. When combined with meta-analysis this method can be used to understand the scope of bias associated with a specific area of research.
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Dehejia is an Indian surname:
Petra Elisabeth (Crockett) Todd is an American economist whose research interests include labor economics, development economics, microeconomics, and econometrics. She is the Edward J. and Louise W. Kahn Term Professor of Economics at the University of Pennsylvania, and is also affiliated with the University of Pennsylvania Population Studies Center, the Human Capital and Equal Opportunity Global Working Group (HCEO), the IZA Institute of Labor Economics and the National Bureau of Economic Research.
Barbara Sianesi is an Italian economist currently a senior research economist at the Institute for Fiscal Studies in London. She obtained her PhD from University College London and a BA in economics from Bocconi University.
Jasjeet "Jas" Singh Sekhon is a data scientist, political scientist, and statistician at Yale University. Sekhon is the Eugene Meyer Professor at Yale University, a fellow of the American Statistical Association, and a fellow of the Society for Political Methodology. Sekhon's primary research interests lie in causal inference, machine learning, and their intersection. He has also published research on their application in various fields including voting behavior, online experimentation, epidemiology, and medicine.
References
- ↑ Dehejia, Rajeev, and Sadek Wahba (1999), "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," Journal of the American Statistical Association, Volume 94, Number 448 (December 1999), pp. 1053–1062.
- ↑ Dehejia, Rajeev, and Sadek Wahba, "Propensity Score Matching Methods for Non-Experimental Causal Studies," Review of Economics and Statistics, Volume 84 (February 2002), pp. 151–161.
