The reliability of controlled experiments, or "A/B tests," can often be
compromised due to the phenomenon of network interference, wherein the outcome
for one unit is influenced by other units. To tackle this challenge, we propose
a machine learning-based method to identify and characterize heterogeneous
network interference. Our approach accounts for latent complex network
structures and automates the task of "exposure mapping'' determination, which
addresses the two major limitations in the existing literature. We introduce
"causal network motifs'' and employ transparent machine learning models to
establish the most suitable exposure mapping that reflects underlying network
interference patterns. Our method's efficacy has been validated through
simulations on two synthetic experiments and a real-world, large-scale test
involving 1-2 million Instagram users, outperforming conventional methods such
as design-based cluster randomization and analysis-based neighborhood exposure
mapping. Overall, our approach not only offers a comprehensive, automated
solution for managing network interference and improving the precision of A/B
testing results, but it also sheds light on users' mutual influence and aids in
the refinement of marketing strategies.

提出了一种基于机器学习的方法来识别和表征异质网络干扰，该方法通过引入因果网络模式和透明机器学习模型来建立反映潜在网络干扰模式的最适 exposure mapping，为管理网络干扰和提高 A/B 测试精确性提供了全面的自动化解决方案。

一种两部分的机器学习方法用于表征 A/B 测试中的网络干扰

A Two-Part Machine Learning Approach to Characterizing Network  Interference in A/B Testing

Performing controlled experiments on noisy data is essential in understanding
deep learning across noise levels. Due to the lack of suitable datasets,
previous research has only examined deep learning on controlled synthetic label
noise, and real-world label noise has never been studied in a controlled
setting. This paper makes three contributions. First, we establish the first
benchmark of controlled real-world label noise from the web. This new benchmark
enables us to study the web label noise in a controlled setting for the first
time. The second contribution is a simple but effective method to overcome both
synthetic and real noisy labels. We show that our method achieves the best
result on our dataset as well as on two public benchmarks (CIFAR and
WebVision). Third, we conduct the largest study by far into understanding deep
neural networks trained on noisy labels across different noise levels, noise
types, network architectures, and training settings. The data and code are
released at the following link: this http URL

本文通过建立了第一个来自网络的真实标签噪声控制基准，解决了以往研究只着眼于控制合成标签噪声的缺点，并通过简单而有效的方法克服了合成和真实噪声标签，对不同噪声水平、噪声类型、网络结构和训练设置等方面进行了迄今为止最大的研究，以深入了解深度神经网络在噪声标签训练中的表现。

控制噪声标签的深度学习：超越合成噪声

Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels

Choosing optimal (or at least better) policies is an important problem in
domains from medicine to education to finance and many others. One approach to
this problem is through controlled experiments/trials - but controlled
experiments are expensive. Hence it is important to choose the best policies on
the basis of observational data. This presents two difficult challenges: (i)
missing counterfactuals, and (ii) selection bias. This paper presents
theoretical bounds on estimation errors of counterfactuals from observational
data by making connections to domain adaptation theory. It also presents a
principled way of choosing optimal policies using domain adversarial neural
networks. We illustrate the effectiveness of domain adversarial training
together with various features of our algorithm on a semi-synthetic breast
cancer dataset and a supervised UCI dataset (Statlog).

本文通过控制实验方法和观察性数据建立了最优决策的理论模型，并利用域对抗神经网络的方法进行决策选择，从而在处理缺失反事实和选择偏差方面取得了较好的效果。