{"ID":5938036,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-07T19:36:44.467391193Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03999","arxiv_id":"2607.03999","title":"Significance-First Splitting: Aligning Treatment Heterogeneity Detection with Honest Estimation","abstract":"Estimating heterogeneous treatment effects (CATE) requires simultaneously detecting effect modification and quantifying estimation uncertainty. Existing tree-based methods make an uneasy trade-off: significance-based approaches (Radcliffe and Surry 2011) identify subgroup interactions directly but lack valid inference; honest causal trees (Athey and Imbens 2016) deliver nominal confidence interval coverage but use outcome-agnostic splitting criteria that sacrifice interaction sensitivity. We introduce a hybrid algorithm that fuses significance-based splitting with honest sample-splitting and cross-validation. Our splitting criterion uses the squared $t$-statistic for the treatment $\\times$ side interaction ($t^2$), which is shown to be directly aligned with the honest $\\text{EMSE}_τ$ criterion when the interaction is strong. Post-hoc honest cross-validation selects the cost-complexity penalty, giving a single principled estimator with nominal CI coverage at the leaf level. For forests, we retain bootstrap count vectors to enable an infinitesimal jackknife (IJ) variance estimate of Monte-Carlo convergence rather than formal pointwise inference. On the three synthetic designs from (Athey and Imbens 2016) the single tree achieves approximately 90\\% leaf-average CI coverage at the 90\\% nominal level across all three designs (200 replications each); on the Criteo and Starbucks uplift datasets we match Qini coefficient performance of S- and T-learner baselines. An open-source Python package with reproducible seeds, sklearn-compatible API, and full test coverage accompanies this work (https://codeberg.org/hadjipantelis/rattus).","short_abstract":"Estimating heterogeneous treatment effects (CATE) requires simultaneously detecting effect modification and quantifying estimation uncertainty. Existing tree-based methods make an uneasy trade-off: significance-based approaches (Radcliffe and Surry 2011) identify subgroup interactions directly but lack valid inference;...","url_abs":"https://arxiv.org/abs/2607.03999","url_pdf":"https://arxiv.org/pdf/2607.03999v1","authors":"[\"Pantelis Z. Hadjipantelis\",\"Josephine Chiang\",\"Karthik Nagesh\"]","published":"2026-07-04T19:50:35Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"cs.LG\",\"stat.ML\"]","methods":"[]","project_urls":"[\"https://codeberg.org/hadjipantelis/rattus\"]","has_code":false}