Rajeev Dehejia

Last updated
Rajeev H. Dehejia
Born1973 (age 4950)
NationalityCanadian
Indian
American
Academic career
Institution New York University
Field Labor economics, Econometrics, Development economics
Alma mater Harvard University (Ph.D.)
Carleton University (B.A.)
Doctoral
advisor
Gary Chamberlain
Guido Imbens
Information at IDEAS / RePEc

Rajeev Dehejia is a professor of public policy in the Robert F. Wagner Graduate School of Public Service at New York University. He is the author of numerous academic articles in econometrics, labor economics, and development economics, including two widely cited papers on the evaluation of propensity score matching. [1] [2] He graduated in 1988 from Sir Robert Borden High School and in 1992 from Carleton University with the Governor General's Medal. He completed his Ph.D. in economics from Harvard University in 1997.

Related Research Articles

<span class="mw-page-title-main">Field experiment</span> Experiment conducted outside the laboratory

Field experiments are experiments carried out outside of laboratory settings.

<span class="mw-page-title-main">Youssef Wahba</span> Prime Minister of Egypt (1919–1920)

Youssef Wahba Pasha (1852-1934) was an Egyptian Prime Minister and jurist.

<span class="mw-page-title-main">John A. List</span> American economist

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."

<span class="mw-page-title-main">Observational study</span> Study with uncontrolled variable of interest

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.

<span class="mw-page-title-main">Quasi-experiment</span> Empirical interventional study

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.

Thomas Joseph Kane is an American education economist who currently holds the position of Walter H. Gale Professor of Education and Economics at the Harvard Graduate School of Education. He has performed research on education policy, labour economics and econometrics. During Bill Clinton's first term as U.S. President, Kane served on the Council of Economic Advisers.

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.

<span class="mw-page-title-main">Alvin E. Roth</span> American academic (born 1951)

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.

<span class="mw-page-title-main">Guido Imbens</span> Dutch-American econometrician

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.

<span class="mw-page-title-main">Stephen L. Morgan</span> American sociologist (born 1971)

Stephen Lawrence Morgan is a Bloomberg Distinguished Professor of Sociology and Education at the Johns Hopkins University School of Arts and Sciences and Johns Hopkins School of Education. A quantitative methodologist, he is known for his contributions to quantitative methods in sociology as applied to research on schools, particularly in models for educational attainment, improving the study of causal relationships, and his empirical research focusing on social inequality and education in the United States.

<span class="mw-page-title-main">Sadek Wahba</span> American businessman and policy advocate

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.

Adriana Lleras-Muney is a Colombian-American economist. She is currently a professor in the Department of Economics at UCLA. She was appointed as Associate Editor for the Journal of Health Economics in 2014, and she was elected as one of the six members of the American Economic Association Executive committee in 2018. Her research focuses on socio-economic status and health with a particular emphasis on education, income, and economic development. In 2017, she was received the Presidential Early Career Awards for Scientists and Engineers from President Obama.

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

  1. 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.
  2. 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.