Accounting for No-Shows in Experimental Evaluation Designs

By Howard S. Bloom. 1984

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, methodology): “t deals directly with the non-compliance is to use instrumental variables methods and related methods based on principal stratiﬁcation (Frangakis and Rubin, 2002; Barnard, Du, Hill, and Rubin, 1998). Bloom (1984), Zelen (1979, 1990), Baker and Lindeman (1994), Cuzick, Edwards, and Segnan (1997), con”
Inference on endogenously censored regression models using conditional moment inequalities (2009, methodology): “Given the random assignment of the type of treatment, the treatment indicator (TREAT) is a natural choice (see, e.g. Bloom (1984)) of an instrumental variable as it is correlated with LOS.”
Regression-Discontinuity Analysis: A Survey of Recent Developments in Economics (2008, methodology): “Moreover, although the sharp design represents a special case of selection on observables (Heckman and Robb, 1985), common solutions to such selection problems, such as propensity score-matching methods, are not applicable here as the design violates the strong ignorability condition of Rosenbaum an”
Moving From the Lab to the Field: The Role of Fidelity and Achieved Relative Intervention Strength (2009, background): “…approach is often utilized to estimate the effects of an intervention for only those treatment participants who actually received the intervention, that is, complier average causal effect estimation (e.g., Angrist, Imbens, & Rubin, 1996; Bloom, 1984; Jo, Asparouhov, Muthen, Ialongo, & Brown, 2008).”
Making a Difference in the Lives of Infants and Toddlers and Their Families: The Impacts of Early Head Start. Volume II: Final Technical Report Appendixes (2002, background): “In this case, the impact per participant in a site can be obtained by dividing the impact per eligible applicant in that site by the site’s program group participation rate (Bloom 1984).”
Long-Term Neighborhood Effects on Low-Income Families: Evidence from Moving to Opportunity (2013, unknown): “…the effects of treatment on the treated (TOT), which use random assignment indicators as instruments for moving through MTO in the Experimental or Section 8 groups and assume the treatment assignment only affects families who move using a MTO voucher (Bloom 1984; Angrist, Imbens, and Rubin 1996).”
Moving to opportunity: an experimental study of neighborhood effects on mental health. (2003, methodology): “To supplement these regression analyses, which in all likelihood represent an underestimation of the program’s effects because of the relatively low take-up rate, treatment-ontreated (TOT) effects were estimated with 2-stage least squares regression or instrumental variable analysis.(15,16) The firs”
Principal Stratification (2015, background): “In the program evaluation literature, the idea appears to originate in the analysis of the Job Training Partnership Act (Bloom et al., 1997), with further discussion in Orr (1999), and eventually as endogenous or program-related subgroups (Peck, 2003; see Peck, 2013, for a discussion). In statistics”
Bayesian inference for causal effects in randomized experiments with noncompliance (1997, background): “…the discussion in Angrist, Imbens and Rubin (1996)], monotonicity is especially plausible in the context of a randomized trial of a drug whose access is restricted to units assigned to take it, implying Di 0 = 0 for all i, as in the application in Section 6 and in Zelen (1979) and Bloom (1984).”
Trimming for Bounds on Treatment Effects with Missing Outcomes (2002, background): “…1997; Heckman and Vytlacil 1999, 2000a, 2000b) have constructed such bounds to address a different problem – that of imperfect compliance of the treatment, even when “intention” to treat is effectively randomized (Bloom 1984; Robins 1989; Imbens and Angrist, 1994; Angrist, Imbens, and Rubin 1996).”
Statistical Analysis for Multisite Trials Using Instrumental Variables With Random Coefficients (2012, background): “The effect of treatment assignment is potentially important for policy but is not equivalent to the impact of actually participating in the program unless all participants “comply” with their treatment assignment, that is, everyone who is assigned to the novel program participates in that program an”
An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017, methodology): “One analytical approach to accounting for incomplete adoption is to use Complier Average Causal Effect (commonly known as CACE) modeling (3, 9, 60).”
Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT (2014, background): “Assumption 3 dates back to (at least) Bloom (1984), who estimated the effect of a program in the presence of “no shows.””