Interference arises when the treatment assigned to one individual affects the outcomes of other individuals. Commonly, individuals are naturally grouped into clusters, and interference occurs only among individuals within the same cluster, a setting referred to as partial interference. We study network causal effects on outcome quantiles in the presence of partial interference. We develop a general nonparametric efficiency theory for estimating these network quantile causal effects, which leads to a nonparametrically efficient estimator. The proposed estimator is consistent and asymptotically normal with parametric convergence rates, while allowing for flexible, data-adaptive estimation of complex nuisance functions. We leverage a three-way cross-fitting procedure that avoids direct estimation of the conditional outcome distribution. Simulations demonstrate adequate finite-sample performance of the proposed estimators, and we apply the methods to a clustered observational study.

That crisp signal was once a really big deal. The film was made for the military in 1944 and released to the public five years later. Breakthroughs, discoveries, and DIY tips sent six days a week. While fewer and fewer people are listening to FM radio today, it was hot stuff amid its widespread rollout during the late 1930s and early 40s. Short for frequency modulation, FM's appeal compared to AM (amplitude modulation) were immediately apparent: a clearer sound, less static, and more reliable transmissions.

Suppose an online platform wants to compare a treatment and control policy, e.g.,

Convergence to a simplex ETF: Class means tend towards equinorm and equiangular vectors when centred about the global average.

Specifically, we learn the data representation as a task-specific composition of pre-trained token representations shared across all tasks. Then the correlations between different tasks' data representations are grounded

To address this issue, multi-domain joint training ( i.e., multi-dataset joint training) should be introduced into point cloud based 3D object detection, to allow 3D detectors to learn from point clouds of