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).

Large language models (LLMs) offer an inexpensive yet powerful way to annotate text, but are often inconsistent when compared with experts.These errors can bias downstream estimates of population parameters such as regression coefficients and causal effects.To mitigate this bias, researchers have developed debiasing methods such as Design-based Supervised Learning (DSL) and Prediction-Powered Inference (PPI), which promise valid estimation by combining LLM annotations with a limited number of expensive expert annotations.Although these methods produce consistent estimates under theoretical assumptions, it is unknown how they compare in finite samples of sizes encountered in applied research.We make two contributions: First, we study how each method's performance scales with the number of expert annotations, highlighting regimes where LLM bias or limited expert labels significantly affect results.Second, we compare DSL and PPI across a range of tasks, finding that although both achieve low bias with large datasets, DSL often outperforms PPI on bias reduction and empirical efficiency, but its performance is less consistent across datasets.Our findings indicate that there is a bias-variance tradeoff at the level of debiasing methods, calling for more research on developing metrics for quantifying their efficiency in finite samples.

Reducing global poverty, particularly in low- and middle-income countries, is a critical objective of the sustainable development goals (SDGs). To track progress toward these goals, high-frequency, granular geo-temporal data that capture changes at the neighborhood level is essential for researchers and policymakers. Recent advancements in methodology have combined machine learning (ML) and Earth observations (EOs) for poverty estimation, thereby addressing significant data gaps. However, a notable limitation of these EO-ML methods is their frequent deployment without a robust mechanism to quantify predictive uncertainty. Understanding this uncertainty is crucial for making informed decisions, effectively managing risks, and instilling confidence in users and stakeholders regarding the model’s predictions. Although deep learning (DL) offers methods to quantify predictive uncertainties, their reliability is often constrained, failing to accurately reflect the underlying variations in predictions. Our proposed method aims to enhance confidence in predictive uncertainty without sacrificing accuracy. It begins by integrating an external model to explicitly capture data variability. Subsequently, we employ two orthogonal metrics—accuracy and uncertainty—to evaluate the influence of training data, especially in scenarios involving satellite imagery (e.g., selecting a subset of source domain countries for prediction in the target country). By applying these metrics, we formulate criteria to assess the importance of choosing specific countries from the source domain as training data. Our analysis highlights the effectiveness of this methodology in situations where the target country offers high-dimensional data, like satellite images, but faces a shortage of adequate training samples for DL models.

To combat poor health and living conditions, policymakers in Africa require temporally and geographically granular data measuring economic well-being. Machine learning (ML) offers a promising alternative to expensive and time-consuming survey measurements by training models to predict economic conditions from freely available satellite imagery. However, previous efforts have failed to utilize the temporal information available in earth observation (EO) data, which may capture developments important to standards of living. In this work, we develop an EO-ML method for inferring neighborhood-level material-asset wealth using multi-temporal imagery and recurrent convolutional neural networks. Our model outperforms state-of-the-art models in several aspects of generalization, explaining 72% of the variance in wealth across held-out countries and 75% held-out time spans. Using our geographically and temporally aware models, we created spatio-temporal material-asset data maps covering the entire continent of Africa from 1990 to 2019, making our data product the largest dataset of its kind. We showcase these results by analyzing which neighborhoods are likely to escape poverty by the year 2030, which is the deadline for when the Sustainable Development Goals (SDG) are evaluated.

Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average causal effect (ACE) and conditional ACE (CACE). Traditional causal effect estimation methods such as Inverse Probability Weighting (IPW) and more recently Regression-With-Residuals (RWR) are widely used - as they avoid the challenging task of identifying the SCM parameters - to estimate ACE and CACE. However, much work remains before traditional estimation methods can be used for counterfactual inference, and for the benefit of Personalized Public Policy Analysis (P3A) in the social sciences. While doctors rely on personalized medicine to tailor treatments to patients in laboratory settings (relatively closed systems), P3A draws inspiration from such tailoring but adapts it for open social systems. In this article, we develop a method for counterfactual inference that we name causal-Graphical Normalizing Flow (c-GNF), facilitating P3A. A major advantage of c-GNF is that it suits the open system in which P3A is conducted. First, we show how c-GNF captures the underlying SCM without making any assumption about functional forms. This capturing capability is enabled by the deep neural networks that model the underlying SCM via observational data likelihood maximization using gradient descent. Second, we propose a novel dequantization trick to deal with discrete variables, which is a limitation of normalizing flows in general. Third, we demonstrate in experiments that c-GNF performs on-par with IPW and RWR in terms of bias and variance for estimating the ATE, when the true functional forms are known, and better when they are unknown. Fourth and most importantly, we conduct counterfactual inference with c-GNFs, demonstrating promising empirical performance. Because IPW and RWR, like other traditional methods, lack the capability of counterfactual inference, c-GNFs will likely play a major role in tailoring personalized treatment, facilitating P3A, optimizing social interventions - in contrast to the current `one-size-fits-all' approach of existing methods.

Causal inference methods that control for textbased confounders are becoming increasingly important in the social sciences and other disciplines where text is readily available.However, these methods rely on a critical assumption that there is no treatment leakage: that is, the text only contains information about the confounder and no information about treatment assignment.When this assumption does not hold, methods that control for text to adjust for confounders face the problem of posttreatment (collider) bias.However, the assumption that there is no treatment leakage may be unrealistic in real-world situations involving text, as human language is rich and flexible.Language appearing in a public policy document or health records may refer to the future and the past simultaneously, and thereby reveal information about the treatment assignment.In this article, we define the treatment-leakage problem, and discuss the identification as well as the estimation challenges it raises.Second, we delineate the conditions under which leakage can be addressed by removing the treatment-related signal from the text in a preprocessing step we define as text distillation.Lastly, using simulation, we show how treatment leakage introduces a bias in estimates of the average treatment effect (ATE) and how text distillation can mitigate this bias.