Measurement for Opaque Systems: Multi-source Triangulation with Interpretable Machine Learning

• URL: http://arxiv.org/abs/2602.00022v1
• Date: Fri, 16 Jan 2026 20:09:53 GMT
• Title: Measurement for Opaque Systems: Multi-source Triangulation with Interpretable Machine Learning
• Authors: Margaret Foster,
• Abstract summary: We propose a measurement framework that uses indirect data traces, interpretable machine-learning models, and theory-guided triangulation to fill inaccessible measurement spaces.<n>Our framework provides an analytical workflow tailored to quantitative characterization in the absence of data sufficient for conventional statistical or causal inference.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a measurement framework for difficult-to-access contexts that uses indirect data traces, interpretable machine-learning models, and theory-guided triangulation to fill inaccessible measurement spaces. Many high-stakes systems of scientific and policy interest are difficult, if not impossible, to reach directly: dynamics of interest are unobservable, data are indirect and fragmented across sources, and ground truth is absent or concealed. In these settings, available data often do not support conventional strategies for analysis, such as statistical inference on a single authoritative data stream or model validation against labeled outcomes. To address this problem, we introduce a general framework for measurement in data regimes characterized by structurally missing or adversarial data. We propose combining multi-source triangulation with interpretable machine learning models. Rather than relying on accuracy against unobservable, unattainable ideal data, our framework seeks consistency across separate, partially informative models. This allows users to draw defensible conclusions about the state of the world based on cross-signal consistency or divergence from an expected state. Our framework provides an analytical workflow tailored to quantitative characterization in the absence of data sufficient for conventional statistical or causal inference. We demonstrate our approach and explicitly surface inferential limits through an empirical analysis of organizational growth and internal pressure dynamics in a clandestine militant organization, drawing on multiple observational signals that individually provide incomplete and biased views of the underlying process. The results show how triangulated, interpretable ML can recover substantively meaningful variation.

Related papers

• Robust Molecular Property Prediction via Densifying Scarce Labeled Data [53.24886143129006]
  In drug discovery, compounds most critical for advancing research often lie beyond the training set.<n>We propose a novel bilevel optimization approach that leverages unlabeled data to interpolate between in-distribution (ID) and out-of-distribution (OOD) data.
  arXiv  Detail & Related papers  (2025-06-13T15:27:40Z)
• Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation [0.0]
  Causal inference typically assumes centralized access to individual-level data.<n>We address this problem by estimating the Average Treatment Effect (ATE) from decentralized observational data via a Federated Learning (FL) approach.
  arXiv  Detail & Related papers  (2025-05-23T14:32:57Z)
• Testing Generalizability in Causal Inference [3.547529079746247]
  No formal procedure exists for statistically evaluating generalizability in machine learning algorithms.<n>We propose a systematic framework for statistically evaluating the generalizability of high-dimensional causal inference models.
  arXiv  Detail & Related papers  (2024-11-05T11:44:00Z)
• Conditional Feature Importance for Mixed Data [1.6114012813668934]
  We develop a conditional predictive impact (CPI) framework with knockoff sampling. We show that our proposed workflow controls type I error, achieves high power and is in line with results given by other conditional FI measures. Our findings highlight the necessity of developing statistically adequate, specialized methods for mixed data.
  arXiv  Detail & Related papers  (2022-10-06T16:52:38Z)
• Bias-inducing geometries: an exactly solvable data model with fairness implications [12.532003449620607]
  We introduce an exactly solvable high-dimensional model of data imbalance.<n>We analytically unpack the typical properties of learning models trained in this synthetic framework.<n>We obtain exact predictions for the observables that are commonly employed for fairness assessment.
  arXiv  Detail & Related papers  (2022-05-31T16:27:57Z)
• Non-Linear Spectral Dimensionality Reduction Under Uncertainty [107.01839211235583]
  We propose a new dimensionality reduction framework, called NGEU, which leverages uncertainty information and directly extends several traditional approaches. We show that the proposed NGEU formulation exhibits a global closed-form solution, and we analyze, based on the Rademacher complexity, how the underlying uncertainties theoretically affect the generalization ability of the framework.
  arXiv  Detail & Related papers  (2022-02-09T19:01:33Z)
• OR-Net: Pointwise Relational Inference for Data Completion under Partial Observation [51.083573770706636]
  This work uses relational inference to fill in the incomplete data. We propose Omni-Relational Network (OR-Net) to model the pointwise relativity in two aspects.
  arXiv  Detail & Related papers  (2021-05-02T06:05:54Z)
• Graph Embedding with Data Uncertainty [113.39838145450007]
  spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. Most subspace learning methods do not take into consideration possible measurement inaccuracies or artifacts that can lead to data with high uncertainty.
  arXiv  Detail & Related papers  (2020-09-01T15:08:23Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)
• On Disentangled Representations Learned From Correlated Data [59.41587388303554]
  We bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data. We show that systematically induced correlations in the dataset are being learned and reflected in the latent representations. We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels.
  arXiv  Detail & Related papers  (2020-06-14T12:47:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.