Citations (29)
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Zachary K. Collier, Walter L. Leite & Haobai Zhang. (2023) Estimating propensity scores using neural networks and traditional methods: a comparative simulation study. Communications in Statistics - Simulation and Computation 52:9, pages 4545-4560.
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Alberto Guzman-Alvarez, Xu Qin & Paul W. Scott. (2022) Deep Neural Networks for Propensity Score Estimation. Multivariate Behavioral Research 57:1, pages 164-165.
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Luke Keele & Dylan S. Small. (2021) Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference Using Five Empirical Applications. The American Statistician 75:4, pages 355-363.
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Matthew J. Valente, David P. MacKinnon & Gina L. Mazza. (2020) A Viable Alternative When Propensity Scores Fail: Evaluation of Inverse Propensity Weighting and Sequential G-Estimation in a Two-Wave Mediation Model. Multivariate Behavioral Research 55:2, pages 165-187.
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Jessica F. Harding, Pamela A. Morris & Jennifer Hill. (2017) Understanding Associations Between Low-Income Mothers' Participation in Education and Parenting. Journal of Research on Educational Effectiveness 10:4, pages 704-731.
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Nicole Bohme Carnegie, Masataka Harada & Jennifer L. Hill. (2016) Assessing Sensitivity to Unmeasured Confounding Using a Simulated Potential Confounder. Journal of Research on Educational Effectiveness 9:3, pages 395-420.
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Articles from other publishers (21)
Guillaume Louis Martin, Camille Petri, Julian Rozenberg, Noémie Simon, David Hajage, Julien Kirchgesner, Florence Tubach, Louis Létinier & Agnès Dechartres. (2024) A methodological review of the high-dimensional propensity score in comparative-effectiveness and safety-of-interventions research finds incomplete reporting relative to algorithm development and robustness. Journal of Clinical Epidemiology 169, pages 111305.
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Ting‐Hsuan Chang, Trang Quynh Nguyen, Youjin Lee, John W. JacksonElizabeth A. Stuart. (2022) Flexible propensity score estimation strategies for clustered data in observational studies. Statistics in Medicine 41:25, pages 5016-5032.
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Yang Li, Jiangen He, Chuanren Liu & Yanni Ping. (2022) Peer Influence in the Adoption of Video Games. International Journal of E-Business Research 18:1, pages 1-16.
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Heining Cham. 2022. Comprehensive Clinical Psychology. Comprehensive Clinical Psychology
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Luke Keele, Stephen O’Neill & Richard Grieve. (2021) Comparing the Performance of Statistical Adjustment Methods by Recovering the Experimental Benchmark from the REFLUX Trial. Medical Decision Making 41:3, pages 340-353.
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Margaret E. Roberts, Brandon M. Stewart & Richard A. Nielsen. (2020) Adjusting for Confounding with Text Matching. American Journal of Political Science 64:4, pages 887-903.
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Daniel Goller, Michael Lechner, Andreas Moczall & Joachim Wolff. (2020) Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed. Labour Economics 65, pages 101855.
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Jennifer HillAntonio Linero & Jared Murray. (2020) Bayesian Additive Regression Trees: A Review and Look Forward. Annual Review of Statistics and Its Application 7:1, pages 251-278.
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Ali Rafei, Carol A C Flannagan & Michael R Elliott. (2020) Big Data for Finite Population Inference: Applying Quasi-Random Approaches to Naturalistic Driving Data Using Bayesian Additive Regression Trees. Journal of Survey Statistics and Methodology 8:1, pages 148-180.
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Megan S. Schuler & Sherri Rose. (2017) Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies. American Journal of Epidemiology 185:1, pages 65-73.
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Vincent DorieMasataka Harada, Nicole Bohme Carnegie & Jennifer Hill. (2016) A flexible, interpretable framework for assessing sensitivity to unmeasured confounding. Statistics in Medicine 35:20, pages 3453-3470.
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Meghan P. McCormick, Elise Cappella, Erin E. O’Connor & Sandee G. McClowry. (2015) Social-Emotional Learning and Academic Achievement. AERA Open 1:3, pages 233285841560395.
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Luke Keele. (2017) The Statistics of Causal Inference: A View from Political Methodology. Political Analysis 23:3, pages 313-335.
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Samuel E. Tumlinson, Daniel A. Sass & Stephanie M. Cano. (2014) The Search for Causal Inferences: Using Propensity Scores Post Hoc to Reduce Estimation Error With Nonexperimental Research. Journal of Pediatric Psychology 39:2, pages 246-257.
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Benjamin Nagengast, Herbert W. Marsh & Kit-Tai Hau. (2013) Effects of Single-Sex Schooling in the Final Years of High School: A Comparison of Analysis of Covariance and Propensity Score Matching. Sex Roles 69:7-8, pages 404-422.
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Jennifer Hill & Yu-Sung Su. (2013) Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children’s cognitive outcomes. The Annals of Applied Statistics 7:3.
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Kosuke Imai & Marc Ratkovic. (2013) Estimating treatment effect heterogeneity in randomized program evaluation. The Annals of Applied Statistics 7:1.
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