Academic

Proxy-Guided Measurement Calibration

arXiv:2603.09288v1 Announce Type: new Abstract: Aggregate outcome variables collected through surveys and administrative records are often subject to systematic measurement error. For instance, in disaster loss databases, county-level losses reported may differ from the true damages due to variations in on-the-ground data collection capacity, reporting practices, and event characteristics. Such miscalibration complicates downstream analysis and decision-making. We study the problem of outcome miscalibration and propose a framework guided by proxy variables for estimating and correcting the systematic errors. We model the data-generating process using a causal graph that separates latent content variables driving the true outcome from the latent bias variables that induce systematic errors. The key insight is that proxy variables that depend on the true outcome but are independent of the bias mechanism provide identifying information for quantifying the bias. Leveraging this structure,

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Saketh Vishnubhatla, Shu Wan, Andre Harrison, Adrienne Raglin, Huan Liu · · 1 min read · 14 views
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arXiv:2603.09288v1 Announce Type: new Abstract: Aggregate outcome variables collected through surveys and administrative records are often subject to systematic measurement error. For instance, in disaster loss databases, county-level losses reported may differ from the true damages due to variations in on-the-ground data collection capacity, reporting practices, and event characteristics. Such miscalibration complicates downstream analysis and decision-making. We study the problem of outcome miscalibration and propose a framework guided by proxy variables for estimating and correcting the systematic errors. We model the data-generating process using a causal graph that separates latent content variables driving the true outcome from the latent bias variables that induce systematic errors. The key insight is that proxy variables that depend on the true outcome but are independent of the bias mechanism provide identifying information for quantifying the bias. Leveraging this structure, we introduce a two-stage approach that utilizes variational autoencoders to disentangle content and bias latents, enabling us to estimate the effect of bias on the outcome of interest. We analyze the assumptions underlying our approach and evaluate it on synthetic data, semi-synthetic datasets derived from randomized trials, and a real-world case study of disaster loss reporting.

Executive Summary

The article 'Proxy-Guided Measurement Calibration' addresses the issue of systematic measurement error in aggregate outcome variables collected through surveys and administrative records. The authors propose a framework guided by proxy variables to estimate and correct the systematic errors. They model the data-generating process using a causal graph, introducing a two-stage approach to disentangle content and bias latents using variational autoencoders. The approach is evaluated on synthetic data, semi-synthetic datasets, and a real-world case study of disaster loss reporting. The proposed framework provides a novel solution to the problem of outcome miscalibration, which is crucial for accurate decision-making in various fields. However, the approach relies on strong assumptions about the data-generating process and the availability of proxy variables.

Key Points

  • ▸ The article proposes a framework for proxy-guided measurement calibration to correct systematic measurement error in aggregate outcome variables.
  • ▸ The framework relies on a causal graph model and a two-stage approach using variational autoencoders to disentangle content and bias latents.
  • ▸ The approach is evaluated on synthetic data, semi-synthetic datasets, and a real-world case study of disaster loss reporting.

Merits

Strength in methodology

The article introduces a novel and well-structured approach to address the problem of outcome miscalibration. The use of causal graphs and variational autoencoders provides a sophisticated method for disentangling content and bias latents.

Applicability to real-world scenarios

The article demonstrates the practicality of the proposed framework by applying it to a real-world case study of disaster loss reporting, highlighting its potential for accurate decision-making in various fields.

Demerits

Assumption of proxy variables

The approach relies on the availability and quality of proxy variables, which may not always be feasible or reliable in practice.

Assumption of data-generating process

The framework assumes a specific data-generating process, which may not always hold true in real-world scenarios, requiring further investigation and validation.

Expert Commentary

The article presents a significant contribution to the field of statistical analysis, offering a novel and well-structured approach to address the problem of outcome miscalibration. The use of causal graphs and variational autoencoders provides a sophisticated method for disentangling content and bias latents. However, the approach relies on strong assumptions about the data-generating process and the availability of proxy variables, which may limit its applicability in practice. Nevertheless, the article demonstrates the potential of the proposed framework for accurate decision-making in various fields and highlights the importance of careful consideration of measurement error and bias correction.

Recommendations

  • ✓ Further investigation and validation of the proposed framework on a wider range of datasets and real-world scenarios.
  • ✓ Development of more robust and flexible methods for estimating proxy variables and accounting for uncertainty in the data-generating process.

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