A key challenge for robotic systems is to figure out the behavior of another
agent. The capability to draw correct inferences is crucial to derive human
behavior from examples.
Processing correct inferences is especially challenging when (confounding)
factors are not controlled experimentally (observational evidence). For this
reason, robots that rely on inferences that are correlational risk a biased
interpretation of the evidence.
We propose equipping robots with the necessary tools to conduct observational
studies on people. Specifically, we propose and explore the feasibility of
structural causal models with non-parametric estimators to derive empirical
estimates on hand behavior in the context of object manipulation in a virtual
kitchen scenario. In particular, we focus on inferences under (the weaker)
conditions of partial confounding (the model covering only some factors) and
confront estimators with hundreds of samples instead of the typical order of
thousands. Studying these conditions explores the boundaries of the approach
and its viability.
Despite the challenging conditions, the estimates inferred from the
validation data are correct. Moreover, these estimates are stable against three
refutation strategies where four estimators are in agreement. Furthermore, the
causal quantity for two individuals reveals the sensibility of the approach to
detect positive and negative effects.
The validity, stability and explainability of the approach are encouraging
and serve as the foundation for further research.

该论文提出在虚拟厨房场景中使用结构因果模型和非参数估计器进行观察研究，以推断人类在物体操作中的手部行为的实证估计，并探索了这种方法的可行性和边界。

手部操控的实证估计是可以恢复的：朝着个性化和可解释的机器人支持日常活动迈进一步

Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities

For estimating conditional survival functions, non-parametric estimators can
be preferred to parametric and semi-parametric estimators due to relaxed
assumptions that enable robust estimation. Yet, even when misspecified,
parametric and semi-parametric estimators can possess better operating
characteristics in small sample sizes due to smaller variance than
non-parametric estimators. Fundamentally, this is a bias-variance tradeoff
situation in that the sample size is not large enough to take advantage of the
low bias of non-parametric estimation. Stacked survival models estimate an
optimally weighted combination of models that can span parametric,
semi-parametric, and non-parametric models by minimizing prediction error. An
extensive simulation study demonstrates that stacked survival models
consistently perform well across a wide range of scenarios by adaptively
balancing the strengths and weaknesses of individual candidate survival models.
In addition, stacked survival models perform as good as, or better than, the
model selected through cross-validation. Lastly, stacked survival models are
applied to a well-known German breast cancer study.

本文介绍了使用堆叠生存模型来预测生存函数，该模型通过优化预测误差，联合使用参数化、半参数化和非参数化生存模型来获得更好的预测性能，具有适应性、准确性和灵活性，适用于小样本且可避免低偏差带来的方差大的问题。