arXiv:2607. 04315v1 Announce Type: cross Abstract: This paper studies the problem of identifying the treatment that maximizes the expected natural direct potential outcome (NDPO), which captures the potential outcome of an intervention while excluding the pathway transmitted through a mediator that researchers may wish to remove from evaluation.
arXiv:2607. 15577v1 Announce Type: cross Abstract: Causal bandits exploit structural relationships among variables to share information across interventions and accelerate the identification of high-reward decisions.
arXiv:2502. 06577v3 Announce Type: replace-cross Abstract: Causal knowledge can be used to support decision-making problems.
arXiv:2409. 18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret.
arXiv:2508. 02812v3 Announce Type: replace Abstract: Causal graphical models can encode large amounts structural knowledge, both from the background knowledge of domain experts and the structural knowledge discovered from randomized experiments or observational data.
arXiv:2608. 01545v1 Announce Type: cross Abstract: We study the problem of identifying the dominant arm in multi-armed bandits, where the objective is to find the action with the highest probability of exceeding the realized rewards of all other actions.
The paper introduces a new variant of the multi‑armed bandit problem that incorporates dueling feedback—pairwise comparisons of model responses—and heterogeneous sampling costs to identify the best large language model (LLM) from a set with varying query costs. Assuming a Condorcet winner, the authors propose a Track‑and‑Stop style algorithm that guarantees asymptotically optimal cost as the error probability approaches zero. Extensive experiments on synthetic and real‑world data show that this cost‑aware approach consistently outperforms both classical cost‑unaware algorithms and other cost‑aware extensions.
arXiv:2606. 19361v1 Announce Type: cross Abstract: Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available.
The paper tackles selecting a cost‑constrained set of experiments that most effectively tighten bounds on a partially identifiable causal query. It formalizes this as the NP‑hard max‑potency problem, introduces efficient graphical pruning rules to reduce the search space, and demonstrates the approach on synthetic graphs and real NHANES data to estimate the effect of physical activity on diabetes.
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:2608. 06427v1 Announce Type: new Abstract: Generative models can reproduce an observational distribution while encoding an incorrect causal structure.
arXiv:2510. 22298v2 Announce Type: replace-cross Abstract: Uncovering the causal mechanisms of complex real-world systems remains a significant challenge, as these systems often entail high data collection costs and involve unknown interventions.
arXiv:2606. 14650v1 Announce Type: new Abstract: The identification of optimal structures within vast arrays of interconnected data necessitates significant sampling- and computational effort.