In many settings, interventions may be more effective for some individuals
than others, so that targeting interventions may be beneficial. We analyze the
value of targeting in the context of a large-scale field experiment with over
53,000 college students, where the goal was to use "nudges" to encourage
students to renew their financial-aid applications before a non-binding
deadline. We begin with baseline approaches to targeting. First, we target
based on a causal forest that estimates heterogeneous treatment effects and
then assigns students to treatment according to those estimated to have the
highest treatment effects. Next, we evaluate two alternative targeting
policies, one targeting students with low predicted probability of renewing
financial aid in the absence of the treatment, the other targeting those with
high probability. The predicted baseline outcome is not the ideal criterion for
targeting, nor is it a priori clear whether to prioritize low, high, or
intermediate predicted probability. Nonetheless, targeting on low baseline
outcomes is common in practice, for example because the relationship between
individual characteristics and treatment effects is often difficult or
impossible to estimate with historical data. We propose hybrid approaches that
incorporate the strengths of both predictive approaches (accurate estimation)
and causal approaches (correct criterion); we show that targeting intermediate
baseline outcomes is most effective, while targeting based on low baseline
outcomes is detrimental. In one year of the experiment, nudging all students
improved early filing by an average of 6.4 percentage points over a baseline
average of 37% filing, and we estimate that targeting half of the students
using our preferred policy attains around 75% of this benefit.

通过使用‘鼓励’（nudges）来推动大学生在截止日期前更新财政援助申请，当研究者通过异质性治疗效应估计再将学生指派给相应的治疗组时，下一步通过衡量低预测概率及高预测概率的学生对于应用的回归效果，从而验证多种指标中通过靶向中间预线性结果是最有效的方法。

机器学习指导谁：学生财政援助续期上的因果对比预测指向的实地试验

Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field  Experiment on Student Financial Aid Renewal

Equilibrium propagation (EP) is a compelling alternative to the
backpropagation of error algorithm (BP) for computing gradients of neural
networks on biological or analog neuromorphic substrates. Still, the algorithm
requires weight symmetry and infinitesimal equilibrium perturbations, i.e.,
nudges, to estimate unbiased gradients efficiently. Both requirements are
challenging to implement in physical systems. Yet, whether and how weight
asymmetry affects its applicability is unknown because, in practice, it may be
masked by biases introduced through the finite nudge. To address this question,
we study generalized EP, which can be formulated without weight symmetry, and
analytically isolate the two sources of bias. For complex-differentiable
non-symmetric networks, we show that the finite nudge does not pose a problem,
as exact derivatives can still be estimated via a Cauchy integral. In contrast,
weight asymmetry introduces bias resulting in low task performance due to poor
alignment of EP's neuronal error vectors compared to BP. To mitigate this
issue, we present a new homeostatic objective that directly penalizes
functional asymmetries of the Jacobian at the network's fixed point. This
homeostatic objective dramatically improves the network's ability to solve
complex tasks such as ImageNet 32x32. Our results lay the theoretical
groundwork for studying and mitigating the adverse effects of imperfections of
physical networks on learning algorithms that rely on the substrate's
relaxation dynamics.

通过研究广义平衡传播（generalized EP），我们发现权重的非对称性会导致平衡传播算法与反向传播算法的性能比较低，因此我们提出了一种新的恒定目标函数，用于惩罚网络在固定点的雅可比矩阵的功能不对称性，这显著提高了网络解决复杂任务的能力，例如 ImageNet 32x32。

通过雅可比稳态改进非对称权重的均衡传播

Improving equilibrium propagation without weight symmetry through  Jacobian homeostasis

This article aims to provide a theoretical account and corresponding paradigm
for analysing how explainable artificial intelligence (XAI) influences people's
behaviour and cognition. It uses insights from research on behaviour change.
Two notable frameworks for thinking about behaviour change techniques are
nudges - aimed at influencing behaviour - and boosts - aimed at fostering
capability. It proposes that local and concept-based explanations are more
adjacent to nudges, while global and counterfactual explanations are more
adjacent to boosts. It outlines a method for measuring XAI influence and argues
for the benefits of understanding it for optimal, safe and ethical human-AI
collaboration.

本文提出了一个理论框架和相应的范式，用于分析可解释人工智能如何影响人们的行为和认知，结合行为改变的研究洞察。它提出了衡量 AI 影响并理解其对最佳、安全和道德的人机协作的益处的方法，同时认为本地和基于概念的解释更接近于推动，而全局和反事实解释更接近于促进能力。