arXiv subjects

Elad Berkman

Publications and source records attributed to Elad Berkman.

3 recordsLinked to original sources

arXiv2025

An integer programming-based approach to construct exact two-sample binomial tests with maximum power

Traditional hypothesis tests for differences between binomial proportions are at risk of being too liberal (Wald test) or overly conservative (Fisher's exact test). This problem is exacerbated in small samples. Regulators favour exact tests, which provide robust type I error control, even though they may have lower power than non-exact tests. To target an exact test with high power, we extend and evaluate an overlooked approach, proposed in 1969, which determines the rejection region through a binary decision for each outcome vector and uses integer programming to, in line with the Neyman-Pearson paradigm, find an optimal decision boundary that maximizes a power objective subject to type I error constraints. Despite only evaluating the type I error rate for a finite parameter set, our approach guarantees type I error control over the full parameter space. Our results show that the test maximizing average power exhibits remarkable robustness, often showing highest power among comparators while maintaining exact type I error control. The method can be further tailored to prior beliefs by using a weighted average. The findings highlight both the method's practical utility and how techniques from combinatorial optimization can improve statistical methodology.

stat.ME↗

arXiv2024

Robust CATE Estimation Using Novel Ensemble Methods

The estimation of Conditional Average Treatment Effects (CATE) is crucial for understanding the heterogeneity of treatment effects in clinical trials. We evaluate the performance of common methods, including causal forests and various meta-learners, across a diverse set of scenarios, revealing that each of the methods struggles in one or more of the tested scenarios. Given the inherent uncertainty of the data-generating process in real-life scenarios, the robustness of a CATE estimator to various scenarios is critical for its reliability. To address this limitation of existing methods, we propose two new ensemble methods that integrate multiple estimators to enhance prediction stability and performance - Stacked X-Learner which uses the X-Learner with model stacking for estimating the nuisance functions, and Consensus Based Averaging (CBA), which averages only the models with highest internal agreement. We show that these models achieve good performance across a wide range of scenarios varying in complexity, sample size and structure of the underlying-mechanism, including a biologically driven model for PD-L1 inhibition pathway for cancer treatment. Furthermore, we demonstrate improved performance by the Stacked X-Learner also when comparing to other ensemble methods, including R-Stacking, Causal-Stacking and others.

stat.ME↗

arXiv2024

Causal Responder Detection

We introduce the causal responders detection (CARD), a novel method for responder analysis that identifies treated subjects who significantly respond to a treatment. Leveraging recent advances in conformal prediction, CARD employs machine learning techniques to accurately identify responders while controlling the false discovery rate in finite sample sizes. Additionally, we incorporate a propensity score adjustment to mitigate bias arising from non-random treatment allocation, enhancing the robustness of our method in observational settings. Simulation studies demonstrate that CARD effectively detects responders with high power in diverse scenarios.

stat.ME↗