{"ID":2823376,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00200","arxiv_id":"2601.00200","title":"Detecting Unobserved Confounders: A Kernelized Regression Approach","abstract":"Detecting unobserved confounders is crucial for reliable causal inference in observational studies. Existing methods require either linearity assumptions or multiple heterogeneous environments, limiting applicability to nonlinear single-environment settings. To bridge this gap, we propose Kernel Regression Confounder Detection (KRCD), a novel method for detecting unobserved confounding in nonlinear observational data under single-environment conditions. KRCD leverages reproducing kernel Hilbert spaces to model complex dependencies. By comparing standard and higherorder kernel regressions, we derive a test statistic whose significant deviation from zero indicates unobserved confounding. Theoretically, we prove two key results: First, in infinite samples, regression coefficients coincide if and only if no unobserved confounders exist. Second, finite-sample differences converge to zero-mean Gaussian distributions with tractable variance. Extensive experiments on synthetic benchmarks and the Twins dataset demonstrate that KRCD not only outperforms existing baselines but also achieves superior computational efficiency.","short_abstract":"Detecting unobserved confounders is crucial for reliable causal inference in observational studies. Existing methods require either linearity assumptions or multiple heterogeneous environments, limiting applicability to nonlinear single-environment settings. To bridge this gap, we propose Kernel Regression Confounder D...","url_abs":"https://arxiv.org/abs/2601.00200","url_pdf":"https://arxiv.org/pdf/2601.00200v1","authors":"[\"Yikai Chen\",\"Yunxin Mao\",\"Chunyuan Zheng\",\"Hao Zou\",\"Shanzhi Gu\",\"Shixuan Liu\",\"Yang Shi\",\"Wenjing Yang\",\"Kun Kuang\",\"Haotian Wang\"]","published":"2026-01-01T04:26:02Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\"]","methods":"[]","has_code":false}