A/B testing is critical for modern technological companies to evaluate the effectiveness of
newly developed products against standard baselines. This paper studies optimal designs …

Two-sided online matching platforms have been employed in various markets. However,
agents' preferences in present market are usually implicit and unknown and must be learned …

Many modern tech companies, such as Google, Uber, and Didi, use online experiments
(also known as A/B testing) to evaluate new policies against existing ones. While most …

In this paper, we propose a new algorithm for addressing the problem of matching markets
with complementary preferences, where agents' preferences are unknown a priori and must …

In real-world scenarios, datasets collected from randomized experiments are often
constrained by size, due to limitations in time and budget. As a result, leveraging large …

This article studies the benefits of using spatially randomized experimental designs which
partition the experimental area into distinct, non-overlapping units with treatments assigned …

The aim of this article is to establish a causal link between the policies implemented by
technology companies and the outcomes they yield within intricate temporal and/or spatial …

Modeling the interference effect is an important issue in the field of causal inference. Existing
studies rely on explicit and often homogeneous assumptions regarding interference …

In this paper, we propose a new recommendation algorithm for addressing the problem of
two-sided online matching markets with complementary preferences and quota constraints …

Two-sided online matching is a crucial aspect of optimizing social welfare sequentially
within economic frameworks, achieved through pairing participants via third-party platforms …