Active feature acquisition (AFA) agents, crucial in domains like healthcare
where acquiring features is often costly or harmful, determine the optimal set
of features for a subsequent classification task. As deploying an AFA agent
introduces a shift in missingness distribution, it's vital to assess its
expected performance at deployment using retrospective data. In a companion
paper, we introduce a semi-offline reinforcement learning (RL) framework for
active feature acquisition performance evaluation (AFAPE) where features are
assumed to be time-dependent. Here, we study and extend the AFAPE problem to
cover static feature settings, where features are time-invariant, and hence
provide more flexibility to the AFA agents in deciding the order of the
acquisitions. In this static feature setting, we derive and adapt new inverse
probability weighting (IPW), direct method (DM), and double reinforcement
learning (DRL) estimators within the semi-offline RL framework. These
estimators can be applied when the missingness in the retrospective dataset
follows a missing-at-random (MAR) pattern. They also can be applied to
missing-not-at-random (MNAR) patterns in conjunction with appropriate existing
missing data techniques. We illustrate the improved data efficiency offered by
the semi-offline RL estimators in synthetic and real-world data experiments
under synthetic MAR and MNAR missingness.

在这篇研究论文中，我们介绍了一种半离线强化学习框架，用于评估部署活动特征获取性能 (active feature acquisition performance evaluation, AFAPE)。首先，我们将 AFAPE 问题扩展到静态特征环境中，从而为 AFA 代理提供决策特征获取顺序的更大灵活性。然后，在半离线强化学习框架中，我们推导和适应了新的逆概率权重估计器 (inverse probability weighting, IPW)，直接方法估计器 (direct method, DM) 和双重强化学习估计器 (double reinforcement learning, DRL)。这些估计器可应用于具有缺失数据的回顾数据集，无论缺失性是否遵循随机缺失 (missing-at-random, MAR) 模式或非随机缺失 (missing-not-at-random, MNAR) 模式。在合成数据和真实世界数据的实验中，我们展示了半离线强化学习估计器在合成 MAR 和 MNAR 缺失数据下的改进数据效率。

静态特征设置的主动特征获取方法评估

Evaluation of Active Feature Acquisition Methods for Static Feature  Settings

The gold standard for causal model evaluation involves comparing model
predictions with true effects estimated from randomized controlled trials
(RCT). However, RCTs are not always feasible or ethical to perform. In
contrast, conditionally randomized experiments based on inverse probability
weighting (IPW) offer a more realistic approach but may suffer from high
estimation variance. To tackle this challenge and enhance causal model
evaluation in real-world conditional randomization settings, we introduce a
novel low-variance estimator for causal error, dubbed as the pairs estimator.
By applying the same IPW estimator to both the model and true experimental
effects, our estimator effectively cancels out the variance due to IPW and
achieves a smaller asymptotic variance. Empirical studies demonstrate the
improved of our estimator, highlighting its potential on achieving near-RCT
performance. Our method offers a simple yet powerful solution to evaluate
causal inference models in conditional randomization settings without
complicated modification of the IPW estimator itself, paving the way for more
robust and reliable model assessments.

我们引入了一种新的低方差评估因果误差的估计方法，称为 pairs estimator。通过将同样的 IPW 估计器应用于模型和真实实验效果，我们的估计器有效地消除了由于 IPW 引起的方差，并实现了较小的渐近方差。经验证明，我们的估计器的性能得到了改进，几乎能达到 RCT 的性能水平。我们的方法提供了一种简单而强大的解决方案，用于在条件随机化设置中评估因果推断模型，无需对 IPW 估计器本身进行复杂修改，为更健壮可靠的模型评估铺平了道路。

基于条件随机化的高精度因果模型评估

High Precision Causal Model Evaluation with Conditional Randomization

Existing work on Multimodal Sentiment Analysis (MSA) utilizes multimodal
information for prediction yet unavoidably suffers from fitting the spurious
correlations between multimodal features and sentiment labels. For example, if
most videos with a blue background have positive labels in a dataset, the model
will rely on such correlations for prediction, while ``blue background'' is not
a sentiment-related feature. To address this problem, we define a general
debiasing MSA task, which aims to enhance the Out-Of-Distribution (OOD)
generalization ability of MSA models by reducing their reliance on spurious
correlations. To this end, we propose a general debiasing framework based on
Inverse Probability Weighting (IPW), which adaptively assigns small weights to
the samples with larger bias i.e., the severer spurious correlations). The key
to this debiasing framework is to estimate the bias of each sample, which is
achieved by two steps: 1) disentangling the robust features and biased features
in each modality, and 2) utilizing the biased features to estimate the bias.
Finally, we employ IPW to reduce the effects of large-biased samples,
facilitating robust feature learning for sentiment prediction. To examine the
model's generalization ability, we keep the original testing sets on two
benchmarks and additionally construct multiple unimodal and multimodal OOD
testing sets. The empirical results demonstrate the superior generalization
ability of our proposed framework. We have released the code and data to
facilitate the reproduction.

通过减少模型对虚假相关性的依赖，我们提出了一个基于逆概率加权的通用去偏方法，以提升多模态情感分析模型的超出分布泛化能力。通过解耦每种模态的鲁棒特征和偏倚特征，并利用偏倚特征估计偏倚，我们使用逆概率加权来减少对大偏倚样本的影响，从而促进情感预测的鲁棒特征学习。实证结果证明了我们提出的框架具有优越的泛化能力。