# Data Fusion for High-Resolution Estimation

arXiv: https://arxiv.org/abs/2508.14858 · HTML: https://mlsanity.com/paper/2508.14858/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 69 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [Removing Hidden Confounding by Experimental Grounding](https://mlsanity.com/paper/1810.11646/) (2018)
- [Bounds on the conditional and average treatment effect with unobserved confounding factors](https://mlsanity.com/paper/1808.09521/) (2018)
- [Covariate Balancing Propensity Score by Tailored Loss Functions](https://mlsanity.com/paper/1601.05890/) (2016)
- [Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing](https://mlsanity.com/paper/2102.04543/) (2021)
- [Combining multiple observational data sources to estimate causal effects](https://mlsanity.com/paper/1801.00802/) (2018)
- [Flexible sensitivity analysis for observational studies without observable implications](https://mlsanity.com/paper/1809.00399/) (2018)
- [Learning from a Biased Sample](https://mlsanity.com/paper/2209.01754/) (2022)
- [Multi-Source Causal Inference Using Control Variates](https://mlsanity.com/paper/2103.16689/) (2021)
- [Sensitivity analysis under the f-sensitivity models: a distributional robustness perspective](https://mlsanity.com/paper/2203.04373/) (2022)
- [Efficient and robust methods for causally interpretable meta-analysis: transporting inferences from multiple randomized trials to a target population](https://mlsanity.com/paper/1908.09230/) (2019)
- [Data fusion methods for the heterogeneity of treatment effect and confounding function](https://mlsanity.com/paper/2007.12922/) (2020)
- [Policy Learning under Biased Sample Selection](https://mlsanity.com/paper/2304.11735/) (2023)
- [Semiparametric sensitivity analysis: unmeasured confounding in observational studies](https://mlsanity.com/paper/2104.08300/) (2021)
- [Domain constraints improve risk prediction when outcome data is missing](https://mlsanity.com/paper/2312.03878/) (2023)
- [Distributionally Robust Policy Learning under Concept Drifts](https://mlsanity.com/paper/2412.14297/) (2024)
- [Efficient Estimation Under Data Fusion](https://mlsanity.com/paper/2111.14945/) (2021)
- [Data Fusion for Partial Identification of Causal Effects](https://mlsanity.com/paper/2505.24296/) (2025)
- [Is it who you are or where you are? Accounting for compositional differences in cross-site treatment variation](https://mlsanity.com/paper/2103.14765/) (2021)
- [Data fusion using weakly aligned sources](https://mlsanity.com/paper/2308.14836/) (2023)
- [Inferring fine-grained migration patterns across the United States](https://mlsanity.com/paper/2503.20989/) (2025)

## Abstract (arXiv)

High-resolution estimates of population health indicators are critical for precision public health. We propose a method for high-resolution estimation that fuses distinct data sources: an unbiased, low-resolution data source (e.g. aggregated administrative data) and a potentially biased, high-resolution data source (e.g. individual-level online survey responses). We assume that the potentially biased, high-resolution data source is generated from the population under a model of sampling bias where observables can have arbitrary impact on the probability of response but the difference in the log probabilities of response between units with the same observables is linear in the difference between sufficient statistics of their observables and outcomes. Our data fusion method learns a distribution that is closest (in the sense of KL divergence) to the online survey distribution and consistent with the aggregated administrative data and our model of sampling bias. This approach significantly reduces bias in high-resolution estimates compared to baselines that rely on a single data source alone on a testbed that includes repeated measurements of three indicators measured by both the (online) Household Pulse Survey and ground-truth data sources at two geographic resolutions over the same time period.