Partially Identified Treatment Effects Under Imperfect Compliance: The Case of Domestic Violence

Siddique, Zahra

doi:10.1080/01621459.2013.779836

Cited by 23 publications

(17 citation statements)

References 26 publications

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“…Some applications to evaluation of criminal-justice policy include Manski and Nagin (1998), Manski and Pepper (2013), and Siddique (2013). The closest methodological precedent to the present study is the Manski and Pepper (2000) study of monotone instrumental variable assumptions.…”

Section: Introductionmentioning

confidence: 99%

How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions

Manski¹,

Pepper

2018

The Review of Economics and Statistics

110

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Despite dozens of studies, research on crime in the United States has struggled to reach consensus about the impact of right-to-carry (RTC) gun laws. Empirical results are highly sensitive to seemingly minor variations in the data and model. How then should research proceed? We think that policy analysis is most useful if researchers perform inference under a spectrum of assumptions of varying identifying power, recognizing the tension between the strength of assumptions and their credibility. With this in mind, we formalize and apply a class of assumptions that flexibly restrict the degree to which policy outcomes may vary across time and space. Our bounded variation assumptions weaken in various respects the invariance assumptions commonly made by researchers who assume that certain features of treatment response are constant across space or time. Using bounded variation assumptions, we present empirical analysis of the effect of RTC laws on violent and property crimes. We allow the effects to vary across crimes, years and states. To keep the analysis manageable, we focus on drawing inferences for three states -Virginia, Maryland, and Illinois. We find there are no simple answers; empirical findings are sensitive to assumptions, and vary over crimes, years, and states. With some assumptions, the data do not reveal whether RTC laws increase or decrease the crime rate. With others, RTC laws are found to increase some crimes, decrease other crimes, and have effects that vary over time for others.

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Section: Introductionmentioning

confidence: 99%

How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions

Manski¹,

Pepper

2018

The Review of Economics and Statistics

110

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“…Further developments in that area include work on corresponding confidence region procedures (see, e.g., [7,42,8]), improvements with respect to computational feasibility (e.g., [11,2,62,53]), and extensions to generalized correction methods for misclassification [40,32]. Selected applications include [44]'s study of income poverty measures based on coarsened survey data, [33]'s use of register data to evaluate the German unemployment compensation reform and [60,41,58]'s investigation of treatment effects in observational studies. In another direction, approaches aiming at "most committed regions" containing at least a prespecified percentage of the data can be found in e.g., [49] or [54].…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling under partial identification: Distinguishing three types of identification regions in regression analysis with interval data

Schollmeyer

Augustin

2015

International Journal of Approximate Reasoning

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One of the most promising applications of the methodology of imprecise probabilities in statistics is the reliable analysis of interval data (or more generally coarsened data). As soon as one refrains from making strong, often unjustified assumptions on the coarsening process, statistical models are naturally only partially identified and set-valued parameter estimators (identification regions) have to be derived. In this paper we consider linear regression analysis under interval data in the dependent variable. While in the traditional case of neglected imprecision different understandings of regression modeling lead to the same parameter estimators, we now have to distinguish between two different types of identification regions, called (Sharp) Marrow Region (SMR) and (Sharp) Collection Region (SCR) here. In addition, we propose the Set-loss Region (SR) as a compromise between SMR and SCR based on a set-domained loss function. We elaborate and discuss some fundamental properties of these regions and then illustrate the methodology in detail by an example, where the influence of different covariates on wine quality, measured by a coarse rating scale, is investigated. We also compare the different identification regions to classical estimates from a naive analysis and from common interval censorship modeling.

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“…Current practice when using data from an RCT with imperfect compliance involves reporting point-identified causal effects such as the ITT or LATE only. However, a more complete picture emerges if estimation of partially identified causal effects were to be carried out on the same data [8].…”

Section: Summary and Policy Advicementioning

confidence: 99%

“…Previous work of the author contains a larger number of background references for the material presented here [8].…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…Another approach, known as "partial identification," is the estimation of bounds, or the range of values that the causal effect obtained from a perfect RCT might take [2], [4], [6], [7], [8]. This involves replacing the unknown counterfactuals, or treatment effectiveness among those for whom assigned treatment is different from the treatment actually taken, with a range of values these unknown counterfactuals can take.…”

Section: Partial Identificationmentioning

confidence: 99%

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Randomized control trials in an imperfect world

Siddique¹

2014

izawol

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1Pros "Partial identification" methods are easy to construct and straightforward to implement in order to estimate causal effects in RCTs with imperfect compliance. Of particular policy interest is that partial identification methods provide a measure of treatment effectiveness for the entire population. Partial identification methods require less stringent statistical assumptions than alternative approaches. eLeVaTOr PiTChRandomized control trials (RCTs) have become increasingly important as an evidence-based method to evaluate interventions such as government programs and policy initiatives. Frequently, however, RCTs are characterized by "imperfect compliance," in that not all the subjects who are randomly assigned to take a treatment choose to do so. This could result in a failure to identify the treatment effect, or the impact of the treatment on the population. However, useful information on treatment effectiveness can still be recovered by estimating "bounds," or a range of values in which treatment effectiveness can lie. auThOr'S MaiN MeSSaGeRCTs randomly allocate individuals to treatment or control groups and have become the gold standard of policy evaluation. However, non-perfect compliance in many trials means that comparing the average outcomes for the treated and control populations fails to provide an unbiased estimate of the effect of the policy that is of interest. Instead, only a "partial identification" of possible values of the effect of the policy can be recovered. Even so, this still provides policy-relevant information. Research shows that the methods are simple to implement and are based more on what is known rather than what is assumed. ConsPartial identification does not provide a single prediction but rather a range of values that treatment effectiveness can take. The range of possible values that treatment effectiveness can take can be sufficiently wide that it is no longer possible to determine whether the treatment has a positive or negative causal effect. randomized control trials in an imperfect worldHow can we assess the policy effectiveness of randomized control trials when people don't comply?

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Partially Identified Treatment Effects Under Imperfect Compliance: The Case of Domestic Violence

Cited by 23 publications

References 26 publications

How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions

How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions

Statistical modeling under partial identification: Distinguishing three types of identification regions in regression analysis with interval data

Randomized control trials in an imperfect world

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