Estimating the conditional average treatment effect (CATE) from observational
data is relevant for many applications such as personalized medicine. Here, we
focus on the widespread setting where the observational data come from multiple
environments, such as different hospitals, physicians, or countries.
Furthermore, we allow for violations of standard causal assumptions, namely,
overlap within the environments and unconfoundedness. To this end, we move away
from point identification and focus on partial identification. Specifically, we
show that current assumptions from the literature on multiple environments
allow us to interpret the environment as an instrumental variable (IV). This
allows us to adapt bounds from the IV literature for partial identification of
CATE by leveraging treatment assignment mechanisms across environments. Then,
we propose different model-agnostic learners (so-called meta-learners) to
estimate the bounds that can be used in combination with arbitrary machine
learning models. We further demonstrate the effectiveness of our meta-learners
across various experiments using both simulated and real-world data. Finally,
we discuss the applicability of our meta-learners to partial identification in
instrumental variable settings, such as randomized controlled trials with
non-compliance.

从多环境的观测数据中，利用部分识别法通过元学习器估计条件平均治疗效应的边界。

多环境下部分识别的治疗效果的元学习器

Meta-Learners for Partially-Identified Treatment Effects Across Multiple  Environments

Artificial neural networks used for reinforcement learning are structurally
rigid, meaning that each optimized parameter of the network is tied to its
specific placement in the network structure. It also means that a network only
works with pre-defined and fixed input- and output sizes. This is a consequence
of having the number of optimized parameters being directly dependent on the
structure of the network. Structural rigidity limits the ability to optimize
parameters of policies across multiple environments that do not share input and
output spaces. Here, we evolve a set of neurons and plastic synapses each
represented by a gated recurrent unit (GRU). During optimization, the
parameters of these fundamental units of a neural network are optimized in
different random structural configurations. Earlier work has shown that
parameter sharing between units is important for making structurally flexible
neurons We show that it is possible to optimize a set of distinct neuron- and
synapse types allowing for a mitigation of the symmetry dilemma. We demonstrate
this by optimizing a single set of neurons and synapses to solve multiple
reinforcement learning control tasks simultaneously.

人工神经网络用于强化学习是结构僵化的，参数只能固定在预定义的网络结构中，这限制了在不同环境中优化参数的能力。在本研究中，我们通过演化一组神经元和可塑突触来解决这个问题，展示了通过优化不同的随机结构配置下的神经元的参数，可以解决多个强化学习控制任务。

结构灵活的神经网络：为普适智能体进化构建模块

Structurally Flexible Neural Networks: Evolving the Building Blocks for  General Agents

We address the challenge of constructing valid confidence intervals and sets
in problems of prediction across multiple environments. We investigate two
types of coverage suitable for these problems, extending the jackknife and
split-conformal methods to show how to obtain distribution-free coverage in
such non-traditional, hierarchical data-generating scenarios. Our contributions
also include extensions for settings with non-real-valued responses and a
theory of consistency for predictive inference in these general problems. We
demonstrate a novel resizing method to adapt to problem difficulty, which
applies both to existing approaches for predictive inference with hierarchical
data and the methods we develop; this reduces prediction set sizes using
limited information from the test environment, a key to the methods' practical
performance, which we evaluate through neurochemical sensing and species
classification datasets.

在多个环境下进行预测的情况中，我们解决了构建有效置信区间和集合的挑战。通过扩展自助法和分割一致性方法，我们研究了适用于这些问题的两种覆盖类型，展示了如何在这些非传统的分层数据生成场景中获得无分布覆盖。我们还在非实数值响应的设置中进行了扩展，并在这些一般问题中建立了一致性理论。我们演示了一种新的调整方法以适应问题的难度，该方法既适用于具有分层数据的预测推断的现有方法，又适用于我们开发的方法；通过神经化学感应和物种分类数据集，我们评估了方法的实际性能。

多环境场景下的预测推理

Predictive Inference in Multi-environment Scenarios

Consider learning an imitation policy on the basis of demonstrated behavior
from multiple environments, with an eye towards deployment in an unseen
environment. Since the observable features from each setting may be different,
directly learning individual policies as mappings from features to actions is
prone to spurious correlations -- and may not generalize well. However, the
expert's policy is often a function of a shared latent structure underlying
those observable features that is invariant across settings. By leveraging data
from multiple environments, we propose Invariant Causal Imitation Learning
(ICIL), a novel technique in which we learn a feature representation that is
invariant across domains, on the basis of which we learn an imitation policy
that matches expert behavior. To cope with transition dynamics mismatch, ICIL
learns a shared representation of causal features (for all training
environments), that is disentangled from the specific representations of noise
variables (for each of those environments). Moreover, to ensure that the
learned policy matches the observation distribution of the expert's policy,
ICIL estimates the energy of the expert's observations and uses a
regularization term that minimizes the imitator policy's next state energy.
Experimentally, we compare our methods against several benchmarks in control
and healthcare tasks and show its effectiveness in learning imitation policies
capable of generalizing to unseen environments.

基于多个环境中的行为演示来学习模仿策略，通过学习跨域不变的特征表示，构建与专家行为匹配的模仿策略，以实现在未见环境中的泛化能力。