arXiv Machine Learning July 1, 2026 By Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu, Linsha Chen

Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference

Read the original on arXiv Machine Learning →

arXiv:2606. 30992v1 Announce Type: cross Abstract: Multicollinearity is a long lasting challenge in observational causal inference, especially in regressions -- highly correlated independent variables make it hard to isolate their individual impacts on outcomes of interest.

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

arXiv AI

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arXiv Machine Learning

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Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization

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arXiv AI

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arXiv Machine Learning

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Estimating Supply Incrementality in Two-sided Marketplaces: A Causal Machine Learning Approach

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arXiv Machine Learning

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From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables

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CDFM: Towards a General-Purpose Causal Discovery Foundation Model

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Estimating Supply Incrementality in Two-sided Marketplaces: A Causal Machine Learning Approach

Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach with Social Survey Applications