The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Accounting for No-Shows in Experimental Evaluation Designs
Abstract
This article examines how to estimate the effect of a program in the presence of no- shows—persons who are assigned to the program but do not participate. The article briefly discusses the methodological problems involved, describes two current experimental evaluations that are subject to these problems, presents several estimators that overcome these problems, outlines the conditions necessary for these estimators to be feasible, and describes two extensions of the analysis that illustrate a potentially broad range of further applications.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“One analytical approach to accounting for incomplete adoption is to use Complier Average Causal Effect (commonly known as CACE) modeling (3, 9, 60).”
From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [9]
Citations
Cites
Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment
Hausman, J. A., & Wise, D. A. (1979) · Econometrica · doi:10.2307/1914193 · Cited by 483
Campbell, D. T. (1971) · Urban Affairs Quarterly · doi:10.1177/107808747100700202 · Cited by 47
Research works printed but not linked: 2
- Metcalf, C.E., 1979, A Research Design for the Employment Opportumty Pilot Projects
- Smith, D.A., 1983, The Downriver Community Conference Economic Readjustment Activity Program: Impact Findings from the First Phase of Operations
Cited by
Sorted by citations
Items 501–532 of 532
Bloom, H. S., & Unterman, R. (2013)
Self-citationEvaluation of the Tanzania Energy Sector Project: Final Update of Design ReportPreprint
Chaplin, D., Mamun, A. A., Miller, C., Protik, A., & Schurrer, J. (2015) · RePEc: Research Papers in Economics
Back to work: Testing reemployment service for displaced workers
Price, R. (1992) · Evaluation and Program Planning · doi:10.1016/0149-7189(92)90070-b
early evidence from a randomized Trial
Springer, M. G., & Winters, M. A. (2009)
Essays on Industrial Organization and Health Care Economics
Brown, Z. Y. (2017) · doi:10.7916/d8fj2v14
Variance Estimation in Evaluations With No-Shows: A Comparison of Methods
Litwok, D., & Peck, L. R. (2018) · American Journal of Evaluation · doi:10.1177/1098214017749318
Dispelling Misconceived Beliefs: Insights from Experiments
Brandts, J. (2020) · DIGITAL.CSIC (Spanish National Research Council (CSIC))
Compliance with Treatment Allocation
Dunn, G. (2008) · Wiley eBooks · doi:10.1002/9780470061596.risk0612
Hallberg, K. (2018) · 2018 APPAM Fall Research Conference: Evidence for Action: Encouraging Innovation and Improvement
Fein, D. J., Maynard, R. A., Baelen, R. N., Shivji, A., & Souvanna, P. (2020)
An Aggregation Scheme for Increased Power in Primary Outcome AnalysisPreprint
Lycurgus, T., & Hansen, B. B. (2021) · arXiv (Cornell University)
Old-Age Pensions and Female Labour Supply in IndiaPreprint
Unnikrishnan, V., & Kunal Sen, A. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3868728
Old-Age Pensions and Female Labour Supply in IndiaPreprint
Unnikrishnan, V., & Kunal Sen, A. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3973817
La evaluacion del desarrollo economico y del empleo a nivel local
OECD (2008) · OECD eBooks · doi:10.1787/9789264044234-es
How Lending Decisions Affect Consumer BehaviorPreprint
Burg, V., & Keil, J. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3450375
Baker, S. G. (2004) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess0271
Baker, S. G. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess0271.pub2
Adherence Adjusted Estimates in Randomized Clinical Trials
Meleth, S. (2022) · Springer eBooks · doi:10.1007/978-3-319-52636-2_124
Simple Tests for Selection: Learning More from Instrumental Variables
Black, D. A., Joo, J., LaLonde, R., Smith, J. W., & Taylor, E. J. (2022) · National Bureau of Economic Research · doi:10.3386/w30291
Martinchek, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4768409
A Conversation with Guido W. Imbens
Mealli, F., & Mortimer, J. H. (2024) · Statistical Science · doi:10.1214/23-sts906
Instrumental Variables with Multiple Time PeriodsPreprint
Picchetti, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4832234
Improving instrumental variable estimators with poststratification
Pashley, N. E., Keele, L. J., & Miratrix, L. W. (2024) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1093/jrsssa/qnae073
The Unintended Benefits of Women's Empowerment on Household SanitationPreprint
Agarwal, M., & Alam, M. M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4992568
Exploring the Potential of Self-Assessment for Teachers’ Development of ICT Competencies and Beliefs
Abbiati, G., Azzolini, D., Balanskat, A., Engelhart, K., Piazzalunga, D., Rettore, E., & Wastiau, P. (2025) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737241310449 · Cited by 4
“Note that when estimating the main treatment effects on the outcomes (both ITT and ATT results, and the corresponding heterogenous analyses), peers are excluded from the regressions.”
InfluentialComplier-Average Causal Effect (CACE) Estimation
Dunn, G. (2025) · International Encyclopedia of Statistical Science · doi:10.1007/978-3-662-69359-9_122
Problems in the Estimation of School Effects
Ludwig, J. (2001) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-60752-790-920251012
Youness, L., & Touhami, N. O. (2026) · International Journal of Educational Development · doi:10.1016/j.ijedudev.2026.103523
Extrapolating Treatment Effects in Multiple-Score Regression Discontinuity Designs
Jiang, W., & Zhu, R. J. B. (2026) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986261426399
Hong, G., Deutsch, J., Kress, P., Trinidad, J. E., & Xu, Z. (2026) · American Journal of Evaluation · doi:10.1177/10982140261438465
“In randomized experiments with noncompliance, the instrumental variable (IV) method has been proposed (Bloom, 1984) to identify and estimate the complier average treatment effect (CATE; Angrist et al., 1996).”
Econometrics for Credible Policy Choice under Uncertainty
Manski, C. F. (2026) · Journal of Political Economy Microeconomics · doi:10.1086/744393
Metadata
- Authors
- Bloom, H. S.
- Affiliations
- Harvard University (US)
- Corresponding author
- Bloom, Howard S.
- Venue
- Evaluation Review
- Publisher
- SAGE Publications
- Published
- 1984-04
- Volume
- 8
- Issue
- 2
- Pages
- 225-246
- ISSN
- 0145-4692 (ISSN-L)
- 0193-841X
- 1552-3926
- OpenAlex
- W2046165968
- Cited by
- 532
- References
- 2
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 15 · 2023: 9 · 2022: 18 · 2021: 13 · 2020: 15 · 2019: 14 · 2018: 14 · 2017: 17 · 2016: 22 · 2015: 22 · 2014: 29 · 2013: 19 · 2012: 38
- FWCI
- 1.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Evaluation Review (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Public Administration › Evaluation and Performance Assessment
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- missed appointments, program participation, program evaluation, experimental design, estimation bias, non-compliance, treatment effect estimation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
