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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.
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“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
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“For a discussion of this adjustment and the conditions under which it is appropriate, see Bloom (1984).”
BackgroundElk, R. A. V., & Kok, S. (2016) · De Economist · doi:10.1007/s10645-016-9281-4 · Cited by 3
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“When using intentionto-treat as instrument and under the monotonicity assumption, the LATE is equal to the average treatment-on-the-treated (ATT) effect as proved in Bloom (1984) and discussed in Angrist and Pischke (2009).”
MethodSanbonmatsu, L., Marvakov, J., Potter, N. A., Yang, F., Adam, E., Congdon, W. J., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kling, J. R., et al. (2000) · Choice Reviews Online · doi:10.5860/choice.37sup-636 · Cited by 3
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“…estimate in column (1) implies access to emergency assistance had a negative impact of 3.7 percentage points, though the estimate is not statistically different from zero.20 The rate of receipt of emergency assistance is critical for converting the ITT estimate of impact to the TOT (Bloom, 1984).”
BackgroundInfluentialFiorentino, F., de la Rosa, C. N., & Day, E. (2022) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.09.015 · Cited by 2
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“In underlying concept if not in its goals, the method relates to instrumental variables estimation (Angrist et al., 1996; Baiocchi et al., 2014; Bloom, 1984) and principal stratification (Frangakis & Rubin, 2002; Page, 2012; Sales & Pane, 2019).”
MethodUmansky, I. M., Cano, M. V., & Porter, L. (2023) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737221144320 · Cited by 3
“…incomplete compliance with treatment assignment using the cut-score rule, for our primary models we adjust our estimated effects using the Wald estimator (Angrist et al., 1996; Bloom, 1984) to provide a local average treatment effect that estimates the effect of EL classification on our outcomes.”
MethodItskovich, E., Buchnik, E., Ariel, B., Wain, N., & Weinborn, C. (2025) · American Journal of Evaluation · doi:10.1177/10982140241313452 · Cited by 3
“The second is the treatment-on-the-treated (TOT) model, which follows the “ treatment-as-delivered ” assumption, analyzing the effects of the intervention only among the units that actually received the treatment as intended (Bloom, 1984, 2008).”
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“As discussed by Bloom (1984) and by Angrist and Imbens (1991), the problem of partial compliance in experiments is that we want to obtain the causal estimate of receiving an intervention (rather than simply being assigned to it), but take-up of the intervention is endogenous (i.e., individuals…”
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“The logic and analysis procedures used to obtain this estimate are presented by Bloom (1984, 1985) .* This estimate is useful in situations where the strategy of attracting participants to the program can be improved, in which case an estimate of the program’s impact can help planners decide whether to invest in incre…”
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“As depicted in Table 1, this method is commonly implemented via two-stage least squares regression, although often the simpler Bloom correction (1984) is used.”
BackgroundMethodPrivileging the Participant: The Importance of Take-Up Rates In Social Welfare EvaluationsPreprint
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Complier-Average Causal Effect (CACE) Estimation
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“Let Zi ∈ {0, 1} be a dummy variable indicating experimental screening offers (3)Our followup horizon matches that in Bretthauer et al. [2022]. We focus on CRC incidence over mortality because estimates for the former are more precise, a point noted by Bretthauer, Løberg and Kaminski [2023]. (4)Angrist and Meager [2023…”
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“If so, then assuming no impact on the remaining 84% would have been reasonable, and the evaluation would have been justified in using a conventional Bloom (1984) correction in computing the treatment’s effects on those who took up the offer.”
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“In Bloom’s words, “Random assignment is necessary to maintain the assumption that the expected program eligible and control group outcomes are the same in the absence of the program” (Bloom, 1984; p. 237).”
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“The principal stratification framework is illustrated by Bloom’s (1984) discus-”
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“…only report ITT estimates throughout this study, but because treatment group non-compliers would have experienced zero impact from the treatment, the effect for students who actually used a scholarship for at least 1 year can be roughly rescaled by dividing the ITT estimates by 0.71 (Bloom, 1984).”
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“%) and there is limited difference between the magnitude of effect of the intent- to-treat (ITT) compared with the effect of the treatment- on- the-treated (TOT), or the local average treatment effect (LATE; Angrist et al., 1996; Bloom, 1984).”
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“Therefore, we applied a “no-show” adjustment using the methodology described in Bloom (1984) to estimate the impact of actually receiving an email.”
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“…school enrollment); and (3) variables related to the enrollment cohort or timing of random assignment (for example, year of random assignment).28 26 Bloom (1984) shows that, under some additional assumptions, ITT estimates can be adjusted to estimate the impact of an intervention on those who…”
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“Under these assumptions, Bloom (1984) and Angrist and Imbens (1994) show that the IV estimator of Yi on Di using randomized Zi as an instrument is the causal effect of the actual treatment of Di on outcome Yi among the subgroup of compliers.”
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Dunn, G. (2005) · Encyclopedia of Biostatistics · doi:10.1002/0470011815.b2a12031
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“Their formal refusers could be viewed as the program group “no-shows” considered by Bloom (1984), and indeed the approach followed by Dubin and Rivers builds upon the Bloom estimator.”
BackgroundBattistin, E., & Rettore, E. (2003) · doi:10.1920/re.ifs.2024.0877
“Our result (as well as Bloom's one) can also be derived as a special case of Angrist and Imbens (1991). The authors prove that, even if participation takes place as a result of self-selection, the mean impact on participants is identifiable provided that (i) there exists a random variable A affecting the participation…”
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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
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