Is Regression Adjustment Supported By the Neyman Model for Causal Inference?

Citations

Cited by:

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6. Peter Z. Schochet, 2018. "Design-Based Estimators for Average Treatment Effects for Multi-Armed RCTs," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 568-593, October.
7. Peter Z. Schochet, "undated". "The Late Pretest Problem in Randomized Control Trials of Education Interventions," Mathematica Policy Research Reports fb514df5dbb84a5dbea79865c, Mathematica Policy Research.
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9. Lu, Jiannan, 2016. "On randomization-based and regression-based inferences for 2K factorial designs," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 72-78.
10. Peter Z. Schochet, 2013. "Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference," Journal of Educational and Behavioral Statistics, , vol. 38(3), pages 219-238, June.
11. Thomas Fraker & Todd Honeycutt & Arif Mamun & Michelle Manno & John Martinez & Bonnie O'Day & Debbie Reed & Allison Thompkins, "undated". "The Social Security Administration's Youth Transition Demonstration Projects: Interim Report on Broadened Horizons, Brighter Futures," Mathematica Policy Research Reports 7698c1fc1bde4b0a8a50f042b, Mathematica Policy Research.
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14. Peter Z. Schochet, 2020. "Analyzing Grouped Administrative Data for RCTs Using Design-Based Methods," Journal of Educational and Behavioral Statistics, , vol. 45(1), pages 32-57, February.
15. Laura Blue & Gregory Peterson & Keith Kranker & Tessa Huffman & Alli Steiner & Amanda Markovitz & Malcolm Williams & Kate Stewart & Julia Rollison & Jia Pu & Thomas Concannon & Liisa Hiatt & Nabeel Qu, "undated". "Evaluation of the Million Hearts® Cardiovascular Disease Risk Reduction Model: Third Annual Report," Mathematica Policy Research Reports d4649f0778804c4eb0adcf2db, Mathematica Policy Research.
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21. Tim Kautz & Peter Z. Schochet & Charles Tilley, "undated". "Comparing Impact Findings from Design-Based and Model-Based Methods: An Empirical Investigation," Mathematica Policy Research Reports b7656ddce20f4007b71836e99, Mathematica Policy Research.
22. Joel A. Middleton, 2021. "Unifying Design-based Inference: On Bounding and Estimating the Variance of any Linear Estimator in any Experimental Design," Papers 2109.09220, arXiv.org.
23. John Deke, 2016. "Design and Analysis Considerations for Cluster Randomized Controlled Trials That Have a Small Number of Clusters," Evaluation Review, , vol. 40(5), pages 444-486, October.
24. Melissa A. Clark & Philip Gleason & Christina Clark Tuttle & Marsha K. Silverberg, 2011. "Do Charter Schools Improve Student Achievement? Evidence from a National Randomized Study," Mathematica Policy Research Reports af41392138504f369930e6f2b, Mathematica Policy Research.
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26. Thomas Fraker & Peter Baird & Alison Black & Arif Mamun & Michelle Manno & John Martinez & Anu Rangarajan & Debbie Reed, "undated". "The Social Security Administration's Youth Transition Demonstration Projects: Interim Report on Colorado Youth WINS," Mathematica Policy Research Reports f57994086f8a436ca69e24800, Mathematica Policy Research.
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29. Peter Z. Schochet, 2021. "Statistical Power for Estimating Treatment Effects Using Difference-in-Differences and Comparative Interrupted Time Series Designs with Variation in Treatment Timing," Papers 2102.06770, arXiv.org, revised Oct 2021.
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32. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2014. "Finite Population Causal Standard Errors," NBER Working Papers 20325, National Bureau of Economic Research, Inc.
33. Peter Z. Schochet, 2010. "The Late Pretest Problem in Randomized Control Trials of Education Interventions," Journal of Educational and Behavioral Statistics, , vol. 35(4), pages 379-406, August.
34. repec:mpr:mprres:6931 is not listed on IDEAS