Year |
Citation |
Score |
2020 |
D’Amour A, Ding P, Feller A, Lei L, Sekhon J. Overlap in observational studies with high-dimensional covariates Journal of Econometrics. DOI: 10.1016/J.Jeconom.2019.10.014 |
0.409 |
|
2019 |
Künzel SR, Sekhon JS, Bickel PJ, Yu B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences of the United States of America. PMID 30770453 DOI: 10.1073/Pnas.1804597116 |
0.389 |
|
2016 |
Bloniarz A, Liu H, Zhang CH, Sekhon JS, Yu B. Lasso adjustments of treatment effect estimates in randomized experiments. Proceedings of the National Academy of Sciences of the United States of America. 113: 7383-90. PMID 27382153 DOI: 10.1073/Pnas.1510506113 |
0.511 |
|
2016 |
O'Neill S, Kreif N, Grieve R, Sutton M, Sekhon JS. Estimating causal effects: considering three alternatives to difference-in-differences estimation. Health Services & Outcomes Research Methodology. 16: 1-21. PMID 27340369 DOI: 10.1007/s10742-016-0146-8 |
0.366 |
|
2015 |
Hartman E, Grieve R, Ramsahai R, Sekhon JS. From sample average treatment effect to population average treatment effect on the treated: Combining experimental with observational studies to estimate population treatment effects Journal of the Royal Statistical Society. Series a: Statistics in Society. 178: 757-778. DOI: 10.1111/rssa.12094 |
0.334 |
|
2014 |
Kreif N, Gruber S, Radice R, Grieve R, Sekhon JS. Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching. Statistical Methods in Medical Research. PMID 24525488 DOI: 10.1177/0962280214521341 |
0.367 |
|
2013 |
Miratrix LW, Sekhon JS, Yu B. Adjusting treatment effect estimates by post-stratification in randomized experiments Journal of the Royal Statistical Society. Series B: Statistical Methodology. 75: 369-396. DOI: 10.1111/J.1467-9868.2012.01048.X |
0.667 |
|
2013 |
Kreif N, Grieve R, Radice R, Sekhon JS. Regression-adjusted matching and double-robust methods for estimating average treatment effects in health economic evaluation Health Services and Outcomes Research Methodology. 13: 174-202. DOI: 10.1007/s10742-013-0109-2 |
0.324 |
|
2012 |
Kreif N, Grieve R, Radice R, Sadique Z, Ramsahai R, Sekhon JS. Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data. Medical Decision Making : An International Journal of the Society For Medical Decision Making. 32: 750-63. PMID 22691446 DOI: 10.1177/0272989X12448929 |
0.316 |
|
2012 |
Sekhon JS, Titiunik R. When natural experiments are neither natural nor experiments American Political Science Review. 106: 35-57. DOI: 10.1017/S0003055411000542 |
0.628 |
|
2011 |
Porter KE, Gruber S, van der Laan MJ, Sekhon JS. The relative performance of targeted maximum likelihood estimators. The International Journal of Biostatistics. 7. PMID 21931570 DOI: 10.2202/1557-4679.1308 |
0.381 |
|
2011 |
Caughey D, Sekhon JS. Elections and the regression discontinuity design: Lessons from close U.S. House Races, 1942-2008 Political Analysis. 19: 385-408. DOI: 10.1093/Pan/Mpr032 |
0.357 |
|
2009 |
Sekhon JS. Opiates for the matches: Matching methods for causal inference Annual Review of Political Science. 12: 487-508. DOI: 10.1146/annurev.polisci.11.060606.135444 |
0.349 |
|
2004 |
Mebane WR, Sekhon JS. Robust Estimation and Outlier Detection for Overdispersed Multinomial Models of Count Data American Journal of Political Science. 48: 392-411. DOI: 10.1111/J.0092-5853.2004.00077.X |
0.659 |
|
2001 |
Brady HE, Herron MC, Mebane WR, Sekhon JS, Shotts KW, Wand J. Law and data: The butterfly ballot episode Ps - Political Science and Politics. 34: 59-69. DOI: 10.1017/S1049096501000099 |
0.63 |
|
2001 |
Wand JN, Shotts KW, Sekhon JS, Mebane WR, Herron MC, Brady HE. The butterfly did it: The aberrant vote for Buchanan in Palm Beach County, Florida American Political Science Review. 95: 793-810. DOI: 10.1017/S000305540040002X |
0.643 |
|
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