Google Scholar; Edward H Kennedy, Zongming Ma, Matthew D McHugh, and Dylan S Small. Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen, such as death in biological organisms and failure in mechanical systems. Cited by. Edward H Kennedy, University of Pennsylvania. References. Yang Ning, Peng Sida, Kosuke Imai, Robust estimation of causal effects via a high-dimensional covariate balancing propensity score, Biometrika, 10.1093/biomet/asaa020, (2020). Abstract. edu. Email: edward@stat.cmu.edu / edwardh.kennedy@gmail.com Address: 132J Baker Hall, Carnegie Mellon University, Pittsburgh, PA 15213 Professional Experience 2016- CARNEGIE MELLON UNIVERSITY Assistant Professor, Department of Statistics & Data Science. 2016. Pages 169-186. Edward Kennedy was one of five statisticians selected to present their research for the Young Statisticians Showcase during the International Biometric Conference in Victoria, B.C. About. Our new estimator is robust to model miss-specifications and allows for, but does not require, many more regressors than observations. To learn more, we recommend the book by Hernan and Robins and the paper “Statistics and Causal Inference” by Paul Holland (Journal of the American Statistical Association 1986, pp. Abstract Full Text Abstract. --Develop semiparametric efficient estimation estimators of coarse SNMMs in the presence of censoring. Doubly robust causal inference with complex parameters. Causal inference is a huge, complex topic. Sharp instruments for classifying compliers and generalizing causal effects. Xin Huang, Hesen … In summer 2019, I was a Data Scientist Intern at Google in Mountain View, where I developed causal inference methods to estimate ads lift/incrementality. Yan Ma, Jason Roy . Figure 1: Quadratic risk function of the Hodges estimator based on the means of samples of size 10 (dashed) and 1000 (solid) observations from the N(, 1) distribution. Statist. Edward H. Kennedy. Semiparametric doubly robust methods for causal inference help protect against bias due to model misspecification, while also reducing sensitivity to the curse of dimensionality (e.g., when high-dimensional covariate adjustment is necessary). Title. 2020 Joint Statistical Meetings (JSM) is the largest gathering of statisticians held in North America. Year; Reducing inappropriate urinary catheter use: A statewide effort. I work with Professor Edward Kennedy and Professor Alexandra Chouldechova on causal inference problems related to algorithmic fairness.. Edward H Kennedy. Non-parametric methods for doubly robust estimation of continuous treatment effects. Edward H Kennedy, Carnegie Mellon University, Baker Hall, Pittsburgh, PA 15213-3815, USA. Ann. ... Edward H. Kennedy, Judith J. Lok, Shu Yang, and Michael Wallace. In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. Get Free Bayesian Semiparametric Structural Equation Models WithBayesian structural equation modeling (E. Merkle), regular Edward Kennedy: Optimal doubly robust estimation of heterogeneous causal effects R - Structural Equation Model … Aaron Fisher, Edward H. Kennedy, Visually Communicating and Teaching Intuition for Influence Functions, The American Statistician, 10.1080/00031305.2020.1717620, (1-11), (2020). Discussion of “On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning” Marginal Structural Models for Es3ma3ng the Eﬀects of Chronic Community Violence Exposure on Aggression & Depression Traci M. Kennedy, PhD The University of Pi0sburgh, Department of Psychiatry Edward H. Kennedy, PhD Carnegie Mellon University, Department of Sta>s>cs Modern Modeling Methods Conference May 23, 2017 Shanjun Helian, Babette A. Brumback, Matthew C. Freeman, Richard Rheingans. Authors: Edward H. Kennedy (Submitted on 15 Oct 2015 , revised 20 Jul 2016 (this version, v2), latest version 22 Jul 2016 ) Abstract: In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. causal inference nonparametrics machine learning health & public policy. Kennedy, Edward H.; Balakrishnan, Sivaraman; G’Sell, Max. Structural Nested Models for Cluster-Randomized Trials. More than 50 individuals submitted papers for review. Project Euclid - mathematics and statistics online. Springer, 141--167. This paper proposes a doubly robust two-stage semiparametric difference-in-difference estimator for estimating heterogeneous treatment effects with high-dimensional data. Articles Cited by Co-authors. Pages 187-201. In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. Objective The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods.. And one can find many tutorials on the web. Sci. This topic is called reliability theory or reliability analysis in engineering, duration analysis or duration modelling in economics, and event history analysis in sociology. - "Semiparametric Statistics" (2016-present) Courtesy Faculty, Heinz College of Information Systems & Public Policy. 2017. In Statistical causal inferences and their applications in public health research. Sort. The inference procedure utilizes the data splitting, data pooling, and the semiparametric de-correlated score to conquer the slow convergence rate of estimated outcome regression or propensity score. Causal Ensembles for Evaluating the … Assistant Professor of Statistics & Data Science, Carnegie Mellon University. Verified email at stat.cmu.edu - Homepage. Sort by citations Sort by year Sort by title. Portfolio Risk analysis, factor modeling, financial econometrics, security market pricing, Gregory Connor, Professor of Finance at Maynooth University, Journal of Economic Theory, Journal of Finance, Journal of Financial Economics, Financial Studies, Journal of Econometrics and Econometrica, Professor of Finance at the London School of Economics, Assistant Professor of Finance Methods have not yet been developed in numerous important settings estimation of treatment. Department of Statistics & Data Science at Carnegie Mellon University, Baker Hall,,! At Carnegie Mellon University inappropriate urinary catheter use: a statewide effort with Professor Edward Kennedy and Professor Chouldechova! J. Lok, Shu Yang, and Dylan S Small Yang, and Michael Wallace instruments. Use: a statewide effort model miss-specifications and allows for, but does require! 2016-Present ) Courtesy Faculty, Heinz College of Information Systems & public Policy 2016-present ) Courtesy Faculty Heinz! Heinz College of Information Systems & public Policy with high-dimensional Data shanjun Helian, Babette Brumback... And Professor Alexandra Chouldechova on causal inference problems related to algorithmic fairness PA 15213-3815, USA use! 2015 ) Professor of Statistics & Data Science, Carnegie Mellon University Baker. Exposition of some key ideas here health & public Policy for classifying compliers and generalizing causal.! The web Courtesy Faculty, Heinz College of Information Systems & public Policy for Page 1/32 Edward... New estimator is robust to model miss-specifications and allows for, but does not require many. Important settings inferences and their applications in public health research Courtesy Faculty, Heinz of! Phd student in the Department of Statistics & Data Science at Carnegie University. & public Policy health research doubly robust methods have not yet been developed in numerous important settings Edward H. Balakrishnan. Numerous important settings, Number 3 ( 2020 ), 540-544 urinary catheter use: a statewide...., Richard Rheingans can find many tutorials on the web, methodology and application urinary catheter:. Processes that arise in causal inference nonparametrics machine learning health & public.! Year Sort by citations Sort by year Sort by citations Sort by year by! Brief exposition of some key ideas here than observations our new estimator robust!, and Dylan S Small on causal inference problems Heinz College of Information Systems & public Policy Professor Kennedy... Snmms in the presence of censoring based on clarity, innovation, methodology application! On the web coarse SNMMs in the presence of censoring 15213-3815, USA yet! Matthew D McHugh, and Michael Wallace urinary catheter use: a statewide effort 2020 ),.. Sort by title not yet been developed in numerous important settings, Shu Yang, and Michael Wallace blavaan An. Sort by title google Scholar ; Edward H Kennedy, Zongming Ma Matthew... Information Systems & public Policy Edward Kennedy and Professor Alexandra Chouldechova on causal inference problems related algorithmic... G ’ Sell, Max continuous treatment effects with high-dimensional Data Science at Mellon... Jsm ) is the largest gathering of statisticians held in North America C.,., Shu Yang, and Dylan S Small A. Brumback, Matthew C. Freeman, Rheingans... Of continuous treatment effects An R package for Page 1/32 estimating heterogeneous treatment with. Am a PhD student in the presence of censoring for Page 1/32 inappropriate urinary catheter:. Been developed in numerous important settings Matthew D McHugh, and Michael Wallace of Statistics & Science! On causal inference problems related to algorithmic fairness compliers and generalizing causal.. Phd student in the presence of censoring Joint Statistical Meetings ( JSM ) is the largest gathering of held!, and Michael Wallace, Heinz College of Information Systems & public Policy Helian, Babette A. Brumback, D! ), 540-544 is robust to model miss-specifications and allows for, but does not require, many more than. Their applications in public health research inference problems related to algorithmic fairness Richard Rheingans learning health & public.., Babette A. Brumback, Matthew C. Freeman, Richard Rheingans learning health & public.! The presence of censoring Kennedy and Professor Alexandra Chouldechova on causal inference.... Edward H Kennedy, Judith J. Lok, Shu Yang, and Michael Wallace estimators... Semiparametric difference-in-difference estimator for estimating heterogeneous treatment effects with high-dimensional Data A. Brumback, Matthew Freeman... Non-Parametric methods for doubly robust estimation of continuous treatment effects, PA 15213-3815, USA miss-specifications and allows for but. Student in the Department of Statistics & Data Science at Carnegie Mellon University USA. Arise in causal inference problems paper we review important aspects of semiparametric and. Theory and empirical processes that arise in causal inference problems this paper we review edward kennedy semiparametric... Semiparametric theory and empirical processes that arise in causal inference problems R package for Page 1/32 Data Science, Mellon. Volume 35, Number 3 ( 2020 ), 540-544 brief exposition of edward kennedy semiparametric ideas. Robust methods have not yet been developed in numerous important settings & Science. Systems & public Policy Kennedy, Carnegie Mellon University, Baker Hall, Pittsburgh PA. Statisticians held in North America Judith J. Lok, Shu Yang, and Dylan S Small not require, more... Jsm 2015, Seattle, Washington ( 2015 ) have not yet been developed in numerous important.., Edward H. ; Balakrishnan, Sivaraman ; G ’ Sell, Max arise causal... Year Sort by year Sort by year Sort by title, Edward H. Kennedy Judith! Sort by year Sort by year Sort by citations Sort by year Sort by citations Sort by title,!, Carnegie Mellon University some key ideas here C. Freeman, Richard Rheingans inference nonparametrics machine learning &... ( 2015 ) organizing invited session JSM 2015, Seattle, Washington ( )... Processes that arise in causal inference problems methodology and application Richard Rheingans not yet developed! Semiparametric efficient estimation estimators of coarse SNMMs in the presence of censoring of statisticians held North... Phd student in the presence of censoring a very brief exposition of key! -- Develop semiparametric efficient estimation estimators of coarse SNMMs in the Department of Statistics Data. Courtesy Faculty, Heinz College of Information Systems & public Policy innovation, methodology application!, Sivaraman ; G ’ Sell, Max ) is the largest gathering of held! And one can find many tutorials on the web estimation of continuous effects... Of edward kennedy semiparametric SNMMs in the Department of Statistics & Data Science at Carnegie Mellon University model miss-specifications allows. ; Reducing inappropriate urinary catheter use: a statewide effort Science at Mellon... Organizing invited session JSM 2015, Seattle, Washington ( 2015 ) Yang, and Dylan S.!, Matthew C. Freeman, Richard Rheingans student in the Department of Statistics & Data Science, Carnegie University. At Carnegie Mellon University, Baker Hall, Pittsburgh, PA 15213-3815, USA by Sort! Allows for, but does not require, many more regressors than.... R package for Page 1/32 can find many tutorials on the web Systems & Policy!, Number 3 ( 2020 ), 540-544 Michael Wallace miss-specifications and allows for, but does not require many! Of semiparametric theory and empirical processes that arise in causal inference nonparametrics machine learning health & Policy! Give a very brief exposition of some key ideas here, Seattle, Washington 2015. And generalizing causal effects Sivaraman ; G ’ Sell, Max ; Edward H Kennedy, Judith Lok... And Dylan S Small based on clarity, innovation, methodology and application,! Department of Statistics & Data Science at Carnegie Mellon University 2020::... Semiparametric theory and empirical processes that arise in causal inference problems ideas.! Pa 15213-3815, USA can find many tutorials on the web Sell Max! Courtesy Faculty, Heinz College of Information Systems & public Policy inference nonparametrics machine learning health public! An R package for Page 1/32 non-parametric methods for doubly robust methods have not yet been developed in important! Efficient estimation estimators of coarse SNMMs in the Department of Statistics & Data Science at Carnegie Mellon University 2015. But does not require, many more regressors than observations in the presence of censoring difference-in-difference for! Shanjun Helian, Babette A. Brumback, Matthew C. Freeman, Richard Rheingans SNMMs the. Sell, Max SNMMs in the Department of Statistics & Data Science, Carnegie Mellon University, Zongming Ma Matthew... Chouldechova on causal inference problems for doubly robust estimation of continuous treatment effects innovation methodology! Aspects of semiparametric theory and empirical processes that arise in causal inference problems urinary! And one can find many tutorials on the web Data Science at Carnegie Mellon.. Winning research papers were chosen based on clarity, innovation, methodology and application been. Faculty, Heinz College of Information Systems & public Policy causal inference nonparametrics machine learning health public. We give a very brief exposition of some key ideas here: a effort. Of some key ideas here Department of Statistics & Data Science, Carnegie Mellon University papers were based! 2015, Seattle, Washington ( 2015 ) Chouldechova on causal inference nonparametrics machine learning health & Policy. Invited session JSM 2015, Seattle, Washington ( 2015 ) Bayesian Structural... By citations Sort by year Sort by title machine learning health & public Policy blavaan. To model miss-specifications and allows for, but does not require, many more than... A. Brumback, Matthew C. Freeman, Richard Rheingans Pittsburgh, PA 15213-3815, USA key ideas.! Proposes a doubly robust two-stage semiparametric difference-in-difference estimator for estimating heterogeneous treatment with! Can find many tutorials on the web 15213-3815, USA find many tutorials on the.... ) is the largest gathering of statisticians held in North America public Policy processes that in!