The paper introduces a framework that fuses data from a small randomized controlled trial (RCT) with a large observational study (OBS) to estimate treatment effects without imposing special assumptions on the OBS. It presents an ATE estimator called AIPW‑Fusion, which uses closed‑form weights and confidence intervals, and two conditional ATE (CATE) learners, DR‑Fusion and R‑Fusion, that preserve unbiasedness while borrowing precision from the OBS. Experiments validate the proposed methods.
arXiv:2603. 19186v3 Announce Type: replace Abstract: Randomized controlled trials (RCTs) are the gold standard for estimating treatment effects, yet they are often underpowered for detecting effect heterogeneity.
arXiv:2609.06294v1 Announce Type: new Abstract: Estimating conditional average treatment effects (CATE) enables efficient targeting of interventions, but many applications have limited experimental s...
The paper introduces a method for selecting the best heterogeneous treatment effect (HTE) estimator from a set of candidates when the true treatment effect is unobserved. It frames estimator selection as a multiple testing problem and proposes a cross‑fitted, exponentially weighted test statistic that uses a two‑way sample splitting scheme to separate nuisance estimation from weight learning, ensuring stability for inference. The authors prove asymptotic familywise error rate control under mild conditions and demonstrate empirically that their procedure reduces false selections compared to common methods on ACIC 2016, IHDP, and Twins benchmarks.
arXiv:2606. 27114v1 Announce Type: new Abstract: Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and debiasing under unobserved confounding scenarios.
arXiv:2609.26142v1 Announce Type: cross Abstract: Augmented inverse-probability weighting (AIPW), targeted maximum likelihood estimation (TMLE), and double/debiased machine learning (DML) are three r...
Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and debiasing under unobserved confounding scenarios. In this paper, we propose the Cross-Head Attention Uplift Network (CHAUN) and Robust Adversarial Inverse Propensity Score (RA-IPS) method to address these limitations.
arXiv:2607. 03999v1 Announce Type: cross Abstract: Estimating heterogeneous treatment effects (CATE) requires simultaneously detecting effect modification and quantifying estimation uncertainty.
arXiv:2503. 20546v2 Announce Type: replace-cross Abstract: We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention, focusing on continuous random variables.
arXiv:2606. 03332v1 Announce Type: new Abstract: Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation.
arXiv:2604. 23904v3 Announce Type: replace-cross Abstract: Synthetic tabular data are often evaluated by distributional similarity, privacy distance, or train-on-synthetic-test-on-real predictive performance, but these criteria do not ensure validity for causal inference.
arXiv:2609.15254v1 Announce Type: cross Abstract: Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual trea...
arXiv:2607. 14346v1 Announce Type: new Abstract: Policy learning methods are increasingly used to inform treatment allocation under budget constraints.