Jasjeet S. Sekhon

Last updated
Jasjeet S. Sekhon
Nationality American and Canadian
Alma mater University of British Columbia (B.A.), Cornell University (M.A., Ph.D.)
Scientific career
Fields Statistics, Data Science, Machine Learning, Causal Inference, Political Science
Institutions Harvard University
University of California, Berkeley
Yale University
Doctoral advisor Walter Mebane

Jasjeet "Jas" Singh Sekhon is a data scientist, political scientist, and statistician at Yale University. Sekhon is the Eugene Meyer Professor at Yale University, [1] a fellow of the American Statistical Association, [2] and a fellow of the Society for Political Methodology. [3] Sekhon's primary research interests lie in causal inference, machine learning, and their intersection. [4] He has also published research on their application in various fields including voting behavior, online experimentation, epidemiology, and medicine.

Contents

Biography

Sekhon graduated with a B.A. from the University of British Columbia. In 1999, he earned a Ph.D. at Cornell University. [3] [5]

Sekhon's career in academia began in 1999, when he became an assistant professor at Harvard University. He stayed at Harvard until 2005 when he moved to UC Berkeley. At Berkeley, he was appointed as the Robson Professor of Political Science and Statistics in 2014. [6] In 2019, he accepted a non-academic position as Head of Advanced Data Science at Bridgewater Associates, and in 2020 left Berkeley to join Yale University, where he was appointed Meyer Professor of Political Science and Statistics and Data Science in 2021. [7] He was named a fellow of the Society for Political Methodology in 2019 [3] and a fellow of the American Statistical Association in 2021. [2]

Sekhon has authored or co-authored dozens of journal articles and several widely used R packages. The topics of his scholarship include experimental research methods, machine learning for estimating causal effects, election fraud, and matching. His research has been widely cited. [8]

Research

Sekhon is best known for his research in causal inference and machine learning. His early research on causal inference focused on the role of matching, but he later wrote an article pointing out that matching is unable to address many of the problems (particularly the selection on observables assumption) that its proponents assume. Nevertheless, his Genetic Matching algorithm remains one of his most highly cited articles. [8] As of 2021, his research focuses on developing interpretable and credible machine learning methods for estimating causal relationships. [5]

One of Sekhon's first publications, a journal article in Digestive Diseases and Sciences, presented a novel treatment, glucocorticoid, for a rare disease that he suffered. Sekhon himself was the first case described in the article. [9]

Selected publications

Related Research Articles

<span class="mw-page-title-main">M. S. Bartlett</span> English statistician

Maurice Stevenson Bartlett FRS was an English statistician who made particular contributions to the analysis of data with spatial and temporal patterns. He is also known for his work in the theory of statistical inference and in multivariate analysis.

<span class="mw-page-title-main">Mathematical statistics</span> Branch of statistics

Mathematical statistics is the application of probability theory, a branch of mathematics, to statistics, as opposed to techniques for collecting statistical data. Specific mathematical techniques which are used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure theory.

<span class="mw-page-title-main">Field experiment</span>

Field experiments are experiments carried out outside of laboratory settings.

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.

Stephen Webb Raudenbush is the Lewis-Sebring Professor of Sociology and Chairman of the Committee on Education at the University of Chicago. He is best known for his development and application of hierarchical linear models (HLM) in the field of education but he has also published on other subjects such as health and crime. Hierarchical linear models, which go by many other names, are used to study many natural processes. To use an example from education, a three level hierarchical model might account for the fact that students are nested in classrooms which are nested in schools. With the right data one could go further and note that schools are nested in districts which are nested in states. Repeated measures of the same individuals can be studied with these models as observations nested in people.

The average treatment effect (ATE) is a measure used to compare treatments in randomized experiments, evaluation of policy interventions, and medical trials. The ATE measures the difference in mean (average) outcomes between units assigned to the treatment and units assigned to the control. In a randomized trial, the average treatment effect can be estimated from a sample using a comparison in mean outcomes for treated and untreated units. However, the ATE is generally understood as a causal parameter that a researcher desires to know, defined without reference to the study design or estimation procedure. Both observational studies and experimental study designs with random assignment may enable one to estimate an ATE in a variety of ways.

<span class="mw-page-title-main">James Robins</span>

James M. Robins is an epidemiologist and biostatistician best known for advancing methods for drawing causal inferences from complex observational studies and randomized trials, particularly those in which the treatment varies with time. He is the 2013 recipient of the Nathan Mantel Award for lifetime achievement in statistics and epidemiology, and a recipient of the 2022 Rousseeuw Prize in Statistics, jointly with Miguel Hernán, Eric Tchetgen-Tchetgen, Andrea Rotnitzky and Thomas Richardson.

<span class="mw-page-title-main">David A. Freedman</span>

David Amiel Freedman was Professor of Statistics at the University of California, Berkeley. He was a distinguished mathematical statistician whose wide-ranging research included the analysis of martingale inequalities, Markov processes, de Finetti's theorem, consistency of Bayes estimators, sampling, the bootstrap, and procedures for testing and evaluating models. He published extensively on methods for causal inference and the behavior of standard statistical models under non-standard conditions – for example, how regression models behave when fitted to data from randomized experiments. Freedman also wrote widely on the application—and misapplication—of statistics in the social sciences, including epidemiology, public policy, and law.

David Collier is an American political scientist specializing in comparative politics. He is Chancellor's Professor Emeritus at the University of California, Berkeley. He works in the fields of comparative politics, Latin American politics, and methodology. His father was the anthropologist Donald Collier.

Matching is a statistical technique which is used to evaluate 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.

Clark N. Glymour is the Alumni University Professor Emeritus in the Department of Philosophy at Carnegie Mellon University. He is also a senior research scientist at the Florida Institute for Human and Machine Cognition.

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 science 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.

<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.

Alan Enoch Gelfand is an American statistician, and is currently the James B. Duke Professor of Statistics and Decision Sciences at Duke University. Gelfand’s research includes substantial contributions to the fields of Bayesian statistics, spatial statistics and hierarchical modeling.

<span class="mw-page-title-main">Henry E. Brady</span> American political scientist

Henry E. Brady is an American political scientist specializing in methodology and its application in a diverse array of political fields. He is Dean of the Goldman School of Public Policy at University of California, Berkeley and holds the Class of 1941 Monroe Deutsch Professor of Political Science and Public Policy. He was elected President of the American Political Science Association, 2009–2010, giving a presidential address entitled "The Art of Political Science: Spatial Diagrams as Iconic and Revelatory." He has published academic works on diverse topics, co-authoring with colleagues at a variety of institutions and ranks, as well as many solo authored works. His principal areas of research are on political behavior in the United States, Canada, and the former Soviet Union, public policy and methodological work on scaling and dimensional analysis. When he became President of the American Political Science Association, a number of his colleagues and co-authors contributed to his presidential biography entitled "Henry Brady, Big Scientist," discussing his work and the fields to which he has contributed and has also shaped.

Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Exploratory causal analysis (ECA), also known as data causality or causal discovery is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions. ECA is a type of causal inference distinct from causal modeling and treatment effects in randomized controlled trials. It is exploratory research usually preceding more formal causal research in the same way exploratory data analysis often precedes statistical hypothesis testing in data analysis

Jennie E. Brand is an American sociologist and social statistician. She studies stratification, social inequality, education, social demography, disruptive events, and quantitative methods, including causal inference. Brand is currently Professor of Sociology and Statistics at the University of California, Los Angeles (UCLA), where she directs the California Center for Population Research and co-directs the Center for Social Statistics.

<span class="mw-page-title-main">Roderick J. A. Little</span> Ph.D. University of London 1974

Roderick Joseph Alexander Little is an academic statistician, whose main research contributions lie in the statistical analysis of data with missing values and the analysis of complex sample survey data. Little is Richard D. Remington Distinguished University Professor of Biostatistics in the Department of Biostatistics at the University of Michigan, where he also holds academic appointments in the Department of Statistics and the Institute for Social Research.

<span class="mw-page-title-main">Sherri Rose</span> American biostatistician

Sherri Rose is an American biostatistician. She is an associate professor of health care policy at Stanford University, having formally worked at Harvard University. A fellow of the American Statistical Association, she has served as co-editor of Biostatistics since 2019 and Chair of the American Statistical Association’s Biometrics Section. Her research focuses on statistical machine learning for health care policy.

References

  1. "Jasjeet Sekhon". Department of Statistics and Data Science. Yale University. Retrieved 4 June 2021.
  2. 1 2 "Jas Sekhon Selected as a Fellow of the American Statistical Association". Department of Statistics and Data Science. Yale University. Retrieved 4 June 2021.
  3. 1 2 3 "Fellows". Cambridge University Press. Society for Political Methodology. Retrieved 4 June 2021.
  4. "Jasjeet Sekhon". Jasjeet Sekhon. Retrieved 3 June 2021.
  5. 1 2 "Jasjeet Sekhon". New Ladder Faculty (2020-2021). Yale University. Retrieved 4 June 2021.
  6. "Curriculum Vitae" (PDF).
  7. "Sekhon named Meyer Professor of Political Science and Statistics and Data". YaleNews.
  8. 1 2 ""Jasjeet S. Sekhon - Google Scholar"". Jasjeet S. Sekhon - Google Scholar. Google Scholar. Retrieved 28 November 2021.
  9. Sekhon, Jasjeet; Chung, Raymond; Epstein, Mark; Kaplan, Marshal (2005). "Steroid-Responsive (Autoimmune?) Sclerosing Cholangitis". Digestive Diseases and Sciences. 50 (10): 1838–1843. doi:10.1007/s10620-005-2948-3. PMID   16187184. S2CID   15494269.