Identification, Data Combination and the Risk of Disclosure

Komarova, Tatiana; Nekipelov, Denis; Yakovlev, Evgeny

doi:10.2139/ssrn.2518493

Cited by 4 publications

(11 citation statements)

References 48 publications

(14 reference statements)

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“…In this case, it is convenient to merge the two samples by linking the records relating to the same unit. There is a rich literature on record linkage which is beyond the scope of this paper [67,68,69,70,71,72]. In the second scenario, the two samples are selected from the same population but have no common unit.…”

Section: Statistical Matchingmentioning

confidence: 99%

Data Integration in Causal Inference

Xu¹,

Pan²,

Wang³

2021

Preprint

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Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This paper reviews development in causal inference methods that combines multiple datasets collected by potentially different designs from potentially heterogeneous populations. We summarize recent advances on combining randomized clinical trial with external information from observational studies or historical controls, combining samples when no single sample has all relevant variables with application to two-sample Mendelian randomization, distributed data setting under privacy concerns for comparative effectiveness and safety research using real-world data, Bayesian causal inference, and causal discovery methods.

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Section: Statistical Matchingmentioning

confidence: 99%

Data Integration in Causal Inference

Xu¹,

Pan²,

Wang³

2021

Preprint

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“…The assumption that the two samples are independent distinguishes our problem from the one with common observational units (e.g., Devereux and Triphati, 2009;Komarova, Nekipelov and Yakovlev, 2012;Poirer and Ziebarth, 2014). The fact that the two samples do not deliver point identification distinguishes this paper from either the case when the two samples jointly deliver point identification (see e.g., Chen, Hong and Tamer, 2005;Hirukawa and Prokhorov, 2014) or the case when one sample alone delivers point identification and a second sample is used for efficiency gains (see e.g., Hellerstein and Imbens, 1999).…”

Section: Related Literaturementioning

confidence: 99%

Two-sample least squares projection

Pacini

2017

Econometric Reviews

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This paper investigates the problem of making inference about the coefficients in the linear projection of an outcome variable y on covariates (x, z) when data are available from two independent random samples; the first sample contains information on only the variables (y, z), while the second sample contains information on only the covariates. In this context, the validity of existing inference procedures depends crucially on the assumptions imposed on the joint distribution of (y, z, x). This paper introduces a novel characterization of the identified set of the coefficients of interest when no assumption (except for the existence of second moments) on this joint distribution is imposed. One finding is that inference is necessarily nonstandard because the function characterizing the identified set is a nondifferentiable (yet directionally differentiable) function of the data. The paper then introduces an estimator and a confidence interval based on the directional differential of the function characterizing the identified set. Monte Carlo experiments explore the numerical performance of the proposed estimator and confidence interval.

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“…We consider the problem of estimating a linearly parametrized utility function with selection problems (in the decision to engage in the utility-producing activity as well as in the decision to reveal the utility derived) when the data is contained in two separate datasets -one «private» and one «public». To do this, we follow a setup similar to (Komarova et al, 2015(Komarova et al, , 2017. However, we make a slight adjustment to the data generating model due to the fact that each observation in our hypothetical «master dataset» is not just a single individual, but an individual-firm combination.…”

Section: Model Setupmentioning

confidence: 99%

“…gives a sequence of thresholds that impose more stringent «rare» identifier frequency and distance requirements on larger samples, and tries to isolate unique matches in the limit (identifier distance of zero). For a more thorough discussion of more general conditions that can be imposed on this sequence, the interested reader is encouraged to read (Komarova et al, 2015(Komarova et al, , 2017.…”

Section: Combining the Data -Decision Rules And K-anonymitymentioning

confidence: 99%

“…For a full discussion of other measures of disclosure risk as well as measures of harm from disclosure, we refer the interested reader to (Komarova et al, 2017) and (Lambert, 1993). Notice that in the above set of anonymity restrictions, the case of the curator imposing = 1 d is equivalent to the curator not imposing any restrictions at all.…”

Section: Combining the Data -Decision Rules And K-anonymitymentioning

confidence: 99%

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K-Anonymity: A Note on the Trade-Off between Data Utility and Data Security

et al. 2017

Self Cite

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Researchers often use data from multiple datasets to conduct credible econometric and statistical analysis. The most reliable way to link entries across such datasets is to exploit unique identifiers if those are available. Such linkage however may result in privacy violations revealing sensitive information about some individuals in a sample. Thus, a data curator with concerns for individual privacy may choose to remove certain individual information from the private dataset they plan on releasing to researchers. The extent of individual information the data curator keeps in the private dataset can still allow a researcher to link the datasets, most likely with some errors, and usually results in a researcher having several feasible combined datasets. One conceptual framework a data curator may rely on is k-anonymity, 2 k ³ , which gained wide popularity in computer science and statistical community. To ensure k-anonymity, the data curator releases only the amount of identifying information in the private dataset that guarantees that every entry in it can be linked to at least k different entries in the publicly available datasets the researcher will use. In this paper, we look at the data combination task and the estimation task from both perspectives -from the perspective of the researcher estimating the model and from the perspective of a data curator who restricts identifying information in the private dataset to make sure that k-anonymity holds. We illustrate how to construct identifiers in practice and use them to combine some entries across two datasets. We also provide an empirical illustration on how a data curator can ensure k-anonymity and consequences it has on the estimation procedure. Naturally, the utility of the combined data gets smaller as k increases, which is also evident from our empirical illustration.

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Identification, Data Combination and the Risk of Disclosure

Cited by 4 publications

References 48 publications

Data Integration in Causal Inference

Data Integration in Causal Inference

Two-sample least squares projection

K-Anonymity: A Note on the Trade-Off between Data Utility and Data Security

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