arXiv Machine Learning August 11, 2026 By Gerrit Gro{\ss}mann, Sumantrak Mukherjee, Sebastian J. Vollmer

CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data

Read the original on arXiv Machine Learning →

arXiv:2608. 08064v1 Announce Type: new Abstract: Learning fine-grained spatial patterns from coarse-resolution data is challenging, especially in causal settings where high-resolution effects must be inferred from aggregated interventions and outcomes.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference

arXiv:2510. 08762v2 Announce Type: replace Abstract: Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, that confound treatment and outcome, and (2) interference from nearby treatments that violate standard no-interference assumptions.

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Causal Inference for Sequential Settings under Interference and Latent Confounding

arXiv:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.

By Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah

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Aug 4

Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference

arXiv:2608. 01352v1 Announce Type: new Abstract: Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference.

By Omar Faruque, Pavan Raj Ravi, Jianwu Wang

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From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification

arXiv:2606. 17010v1 Announce Type: new Abstract: Heterogeneous Treatment Effect (HTE) identification is crucial to explain the impact of an intervention and optimize our policies accordingly.

By Riccardo Cadei, Frank Otchere, Nyasha Tirivayi, Gustavo Angeles Tagliaferro, Falco J. Bargagli-Stoffi, Francesco Locatello

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Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions

arXiv:2606. 07399v1 Announce Type: cross Abstract: Generative models for counterfactual outcomes have great potential to support decision-making under complex interventions, but existing approaches are limited by unstable estimation, poor generalization across environments, and bias from nuisance model misspecification.

By Raphael C Kim, Jingsen Zhu, Ramin Zabih, Michele Santacatterina

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Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions

Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes. Existing datasets either rely on real-world observations without ground-truth counterfactuals or on simplified simulations that fail to capture complex causal dynamics.

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