arXiv:2508.01341v3 Announce Type: replace 
Abstract: Machine learning models trained on Earth observation data, such as satellite imagery, have demonstrated significant promise in predicting household-level wealth indices, enabling the creation of high-resolution wealth maps that can be leveraged across multiple causal trials while addressing chronic data scarcity in global development research. However, because standard training objectives prioritize overall predictive accuracy, these predictions often suffer from shrinkage toward the mean, leading to attenuated estimates of causal treatment effects and limiting their utility in policy evaluations. Existing debiasing methods, such as Prediction-Powered Inference (PPI), can handle this attenuation bias but require additional fresh ground-truth data at the downstream stage of causal inference, which restricts their applicability in data-scarce environments. We introduce and evaluate two post-hoc correction methods -- Linear Calibration Correction (LCC) and a Tweedie's correction approach -- that substantially reduce shrinkage-induced prediction bias without relying on newly collected labeled data. LCC applies a simple linear transformation estimated on a held-out calibration split; Tweedie's method locally de-shrink predictions using density score estimates and a noise scale learned upstream. We provide practical diagnostics for when a correction is warranted and discuss practical limitations. Across analytical results, simulations, and experiments with Demographic and Health Surveys (DHS) data, both approaches reduce attenuation; Tweedie's correction yields nearly unbiased treatment-effect estimates, enabling a "one map, many trials" paradigm. Although we demonstrate on EO-ML wealth mapping, the methods are not geospatial-specific: they apply to any setting where imputed outcomes are reused downstream (e.g., pollution indices, population density, or LLM-derived indicators).

Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven Poverty Analysis

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arXiv:2511.13911v1 Announce Type: cross 
Abstract: Despite recent progress in predicting biomarker trajectories from real clinical data, uncertainty in the predictions poses high-stakes risks (e.g., misdiagnosis) that limit their clinical deployment. To enable safe and reliable use of such predictions in healthcare, we introduce a conformal method for uncertainty-calibrated prediction of biomarker trajectories resulting from randomly-timed clinical visits of patients. Our approach extends conformal prediction to the setting of randomly-timed trajectories via a novel nonconformity score that produces prediction bands guaranteed to cover the unknown biomarker trajectories with a user-prescribed probability. We apply our method across a wide range of standard and state-of-the-art predictors for two well-established brain biomarkers of Alzheimer's disease, using neuroimaging data from real clinical studies. We observe that our conformal prediction bands consistently achieve the desired coverage, while also being tighter than baseline prediction bands. To further account for population heterogeneity, we develop group-conditional conformal bands and test their coverage guarantees across various demographic and clinically relevant subpopulations. Moreover, we demonstrate the clinical utility of our conformal bands in identifying subjects at high risk of progression to Alzheimer's disease. Specifically, we introduce an uncertainty-calibrated risk score that enables the identification of 17.5% more high-risk subjects compared to standard risk scores, highlighting the value of uncertainty calibration in real-world clinical decision making. Our code is available at github.com/vatass/ConformalBiomarkerTrajectories.

تم تطوير طريقة جديدة للتنبؤ بمسارات المؤشرات الحيوية في مرض الزهايمر، حيث تعالج عدم اليقين في التنبؤات السريرية. تتيح هذه التقنية التنبؤية المتوافقة إجراء تنبؤات موثوقة من الزيارات السريرية التي تتم في أوقات عشوائية، مما يضمن أن تغطي نطاقات التنبؤ المسارات الفعلية للمؤشرات الحيوية باحتمالية محددة. تم اختبار النهج باستخدام بيانات التصوير العصبي من دراسات سريرية معروفة، مما يوضح فعاليته في تعزيز أمان التطبيقات السريرية.

Se ha desarrollado un nuevo método para predecir las trayectorias de biomarcadores en la enfermedad de Alzheimer, abordando la incertidumbre en las predicciones clínicas. Esta técnica de predicción conforme permite pronósticos fiables a partir de visitas clínicas programadas aleatoriamente, asegurando que las bandas de predicción cubran las trayectorias reales de los biomarcadores con una probabilidad especificada. El enfoque se probó utilizando datos de neuroimagen de estudios clínicos establecidos, demostrando su eficacia para mejorar la seguridad de las aplicaciones clínicas.

Une nouvelle méthode de prédiction des trajectoires de biomarqueurs dans la maladie d'Alzheimer a été développée, abordant l'incertitude des prédictions cliniques. Cette technique de prédiction conforme permet des prévisions fiables à partir de visites cliniques chronométrées de manière aléatoire, garantissant que les bandes de prédiction couvrent les trajectoires réelles des biomarqueurs avec une probabilité spécifiée. L'approche a été testée à l'aide de données d'imagerie cérébrale provenant d'études cliniques établies, démontrant son efficacité pour améliorer la sécurité des applications cl…

A new method for predicting biomarker trajectories in Alzheimer's disease has been developed, addressing the uncertainty in clinical predictions. This conformal prediction technique allows for reliable forecasts from randomly-timed clinical visits, ensuring that the prediction bands cover the actual biomarker trajectories with a specified probability. The approach was tested using neuroimaging data from established clinical studies, demonstrating its effectiveness in enhancing the safety of clinical applications.

Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands

arXiv:2511.14441v1 Announce Type: cross 
Abstract: To distinguish Markov equivalent graphs in causal discovery, it is necessary to restrict the structural causal model. Crucially, we need to be able to distinguish cause $X$ from effect $Y$ in bivariate models, that is, distinguish the two graphs $X \to Y$ and $Y \to X$. Location-scale noise models (LSNMs), in which the effect $Y$ is modeled based on the cause $X$ as $Y = f(X) + g(X)N$, form a flexible class of models that is general and identifiable in most cases. Estimating these models for arbitrary noise terms $N$, however, is challenging. Therefore, practical estimators are typically restricted to symmetric distributions, such as the normal distribution. As we showcase in this paper, when $N$ is a skewed random variable, which is likely in real-world domains, the reliability of these approaches decreases. To approach this limitation, we propose SkewD, a likelihood-based algorithm for bivariate causal discovery under LSNMs with skewed noise distributions. SkewD extends the usual normal-distribution framework to the skew-normal setting, enabling reliable inference under symmetric and skewed noise. For parameter estimation, we employ a combination of a heuristic search and an expectation conditional maximization algorithm. We evaluate SkewD on novel synthetically generated datasets with skewed noise as well as established benchmark datasets. Throughout our experiments, SkewD exhibits a strong performance and, in comparison to prior work, remains robust under high skewness.

تناقش الورقة الحاجة إلى التمييز بين السبب والأثر في النماذج الثنائية المتغيرة لاكتشاف الأسباب، وخاصة ضمن نماذج الضوضاء ذات الموقع والمقياس (LSNMs). تسلط الضوء على التحدي المتمثل في تقدير هذه النماذج عندما تكون مصطلحات الضوضاء مائلة، وهو أمر شائع في السيناريوهات الواقعية. يقدم المؤلفون SkewD، وهو خوارزمية قائمة على الاحتمالية مصممة لتحسين اكتشاف الأسباب الثنائية في هذه الظروف، مع معالجة القيود التي تواجهها المقدرات التقليدية التي تعتمد عادةً على التوزيعات المتماثلة.

El artículo discute la necesidad de distinguir entre causa y efecto en modelos bivariados para el descubrimiento causal, específicamente dentro de los modelos de ruido de localización-escala (LSNMs). Destaca el desafío de estimar estos modelos cuando los términos de ruido son sesgados, lo cual es común en escenarios del mundo real. Los autores presentan SkewD, un algoritmo basado en la verosimilitud diseñado para mejorar el descubrimiento causal bivariado en estas condiciones, abordando las limitaciones de los estimadores tradicionales que generalmente dependen de distribuciones simétricas.

Cet article aborde la nécessité de distinguer la cause de l'effet dans les modèles bivariés pour la découverte causale, en particulier dans les modèles de bruit de localisation-échelle (LSNMs). Il met en évidence le défi d'estimer ces modèles lorsque les termes de bruit sont asymétriques, ce qui est courant dans les scénarios du monde réel. Les auteurs introduisent SkewD, un algorithme basé sur la vraisemblance conçu pour améliorer la découverte causale bivariée dans ces conditions, en répondant aux limitations des estimateurs traditionnels qui reposent généralement sur des distributions symét…

The paper discusses the need to distinguish between cause and effect in bivariate models for causal discovery, specifically within location-scale noise models (LSNMs). It highlights the challenge of estimating these models when noise terms are skewed, which is common in real-world scenarios. The authors introduce SkewD, a likelihood-based algorithm designed to improve bivariate causal discovery under these conditions, addressing the limitations of traditional estimators that typically rely on symmetric distributions.

Skewness-Robust Causal Discovery in Location-Scale Noise Models

arXiv:2412.09498v3 Announce Type: replace-cross 
Abstract: Gradient descent is one of the most widely used iterative algorithms in modern statistical learning. However, its precise algorithmic dynamics in high-dimensional settings remain only partially understood, which has limited its broader potential for statistical inference applications.
  This paper provides a precise, non-asymptotic joint distributional characterization of gradient descent iterates and their debiased statistics in a broad class of empirical risk minimization problems, in the so-called mean-field regime where the sample size is proportional to the signal dimension. Our non-asymptotic state evolution theory holds for both general non-convex loss functions and non-Gaussian data, and reveals the central role of two Onsager correction matrices that precisely characterize the non-trivial dependence among all gradient descent iterates in the mean-field regime.
  Leveraging the joint state evolution characterization, we show that the gradient descent iterate retrieves approximate normality after a debiasing correction via a linear combination of all past iterates, where the debiasing coefficients can be estimated by the proposed gradient descent inference algorithm. This leads to a new algorithmic statistical inference framework based on debiased gradient descent, which (i) applies to a broad class of models with both convex and non-convex losses, (ii) remains valid at each iteration without requiring algorithmic convergence, and (iii) exhibits a certain robustness to possible model misspecification. As a by-product, our framework also provides algorithmic estimates of the generalization error at each iteration. As canonical examples, we demonstrate our theory and inference methods in the single-index regression model and a generalized logistic regression model, where the natural loss functions may exhibit arbitrarily non-convex landscapes.

يُعتبر الانحدار التدرجي خوارزمية تكرارية تُستخدم على نطاق واسع في التعلم الإحصائي، إلا أن دينامياتها في الإعدادات عالية الأبعاد لا تزال غير مفهومة تمامًا. يقدم هذا البحث توصيفًا مشتركًا غير حدّي لتكرارات الانحدار التدرجي وإحصائياتها المُعالجة في مشاكل تقليل المخاطر التجريبية، لا سيما في نظام الحقل المتوسط. تسلط النتائج الضوء على الدور المركزي لمصفوفات تصحيح أونساجر في فهم الاعتماديات بين جميع تكرارات الانحدار التدرجي، القابلة للتطبيق على كل من دوال الخسارة غير المحدبة والبيانات غير الغاوسية.

El descenso de gradiente es un algoritmo iterativo ampliamente utilizado en el aprendizaje estadístico, pero su dinámica en entornos de alta dimensión no se comprende completamente. Este artículo proporciona una caracterización no asintótica de los iterados del descenso de gradiente y sus estadísticas desbiaseadas en problemas de minimización del riesgo empírico, especialmente en el régimen de campo medio. Los hallazgos destacan el papel central de las matrices de corrección de Onsager en la comprensión de las dependencias entre los iterados del descenso de gradiente, aplicables tanto a funcio…

La descente de gradient est un algorithme itératif largement utilisé dans l'apprentissage statistique, mais sa dynamique dans des contextes de haute dimension n'est pas entièrement comprise. Cet article présente une caractérisation non asymptotique des itérations de la descente de gradient et de leurs statistiques débiaisées dans des problèmes de minimisation du risque empirique, en particulier dans le régime de champ moyen. Les résultats mettent en évidence l'importance des matrices de correction d'Onsager pour comprendre les dépendances entre les itérations de la descente de gradient, applic…

Gradient descent is a widely used iterative algorithm in statistical learning, yet its dynamics in high-dimensional settings are not fully understood. This paper presents a non-asymptotic characterization of gradient descent iterates and their debiased statistics in empirical risk minimization problems, particularly in the mean-field regime. The findings highlight the significance of Onsager correction matrices in understanding the dependencies among gradient descent iterates, applicable to both non-convex loss functions and non-Gaussian data.

Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven Poverty Analysis

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