We introduce the modified planar rotator method (MPRS), a physically inspired
machine learning method for spatial/temporal regression. MPRS is a
non-parametric model which incorporates spatial or temporal correlations via
short-range, distance-dependent ``interactions'' without assuming a specific
form for the underlying probability distribution. Predictions are obtained by
means of a fully autonomous learning algorithm which employs equilibrium
conditional Monte Carlo simulations. MPRS is able to handle scattered data and
arbitrary spatial dimensions. We report tests on various synthetic and
real-word data in one, two and three dimensions which demonstrate that the MPRS
prediction performance (without parameter tuning) is competitive with standard
interpolation methods such as ordinary kriging and inverse distance weighting.
In particular, MPRS is a particularly effective gap-filling method for rough
and non-Gaussian data (e.g., daily precipitation time series). MPRS shows
superior computational efficiency and scalability for large samples. Massive
data sets involving millions of nodes can be processed in a few seconds on a
standard personal computer.

引入了一种改进的平面旋转方法（MPRS），一种以物理为灵感的空间 / 时间回归的机器学习方法，它是一种非参数模型，通过短距离、距离依赖的 “相互作用” 来融合空间或时间相关性，而不假定基础概率分布的具体形式。通过完全自主的学习算法和平衡条件蒙特卡洛模拟来获得预测结果。MPRS 能够处理分散的数据和任意的空间维度。在各种合成和实际数据的测试中，无需参数调整的 MPRS 预测性能与普通克里金和反距离加权等标准插值方法相当。特别是，对于粗糙和非高斯数据（例如，日降水时间序列），MPRS 是一种非常有效的缺口填充方法。MPRS 显示出优越的计算效率和可扩展性，能够在几秒钟内在标准个人计算机上处理涉及数百万个节点的大型数据集。

一种简约、计算高效的机器学习方法用于空间回归

A parsimonious, computationally efficient machine learning method for  spatial regression

Counterfactual inference for continuous rather than binary treatment
variables is more common in real-world causal inference tasks. While there are
already some sample reweighting methods based on Marginal Structural Model for
eliminating the confounding bias, they generally focus on removing the
treatment's linear dependence on confounders and rely on the accuracy of the
assumed parametric models, which are usually unverifiable. In this paper, we
propose a de-confounding representation learning (DRL) framework for
counterfactual outcome estimation of continuous treatment by generating the
representations of covariates disentangled with the treatment variables. The
DRL is a non-parametric model that eliminates both linear and nonlinear
dependence between treatment and covariates. Specifically, we train the
correlations between the de-confounded representations and the treatment
variables against the correlations between the covariate representations and
the treatment variables to eliminate confounding bias. Further, a
counterfactual inference network is embedded into the framework to make the
learned representations serve both de-confounding and trusted inference.
Extensive experiments on synthetic datasets show that the DRL model performs
superiorly in learning de-confounding representations and outperforms
state-of-the-art counterfactual inference models for continuous treatment
variables. In addition, we apply the DRL model to a real-world medical dataset
MIMIC and demonstrate a detailed causal relationship between red cell width
distribution and mortality.

我们提出了一种用于连续治疗的去混淆表示学习 (DRL) 框架，通过生成与治疗变量解耦的协变量表示来进行反事实结果估计。该框架是一个非参数模型，能够消除治疗与协变量之间的线性和非线性依赖关系，并在模型中嵌入了反事实推理网络以实现去混淆和可信的推理。在合成数据集上广泛实验表明，该 DRL 模型在学习去混淆表示方面表现卓越，并且胜过了针对连续治疗的最先进的反事实推理模型。此外，我们将该 DRL 模型应用于实际的医疗数据集 MIMIC，并展示了红细胞宽度分布与死亡率之间的详细因果关系。

通过生成对抗网络对连续处理进行因果推断的解缠表示学习

De-confounding Representation Learning for Counterfactual Inference on  Continuous Treatment via Generative Adversarial Network

With only observational data on two variables, and without other assumptions,
it is not possible to infer which one causes the other. Much of the causal
literature has focused on guaranteeing identifiability of causal direction in
statistical models for datasets where strong assumptions hold, such as additive
noise or restrictions on parameter count. These methods are then subsequently
tested on realistic datasets, most of which violate their assumptions. Building
on previous attempts, we show how to use causal assumptions within the Bayesian
framework. This allows us to specify models with realistic assumptions, while
also encoding independent causal mechanisms, leading to an asymmetry between
the causal directions. Identifying causal direction then becomes a Bayesian
model selection problem. We analyse why Bayesian model selection works for
known identifiable cases and flexible model classes, while also providing
correctness guarantees about its behaviour. To demonstrate our approach, we
construct a Bayesian non-parametric model that can flexibly model the joint. We
then outperform previous methods on a wide range of benchmark datasets with
varying data generating assumptions showing the usefulness of our method.

应用贝叶斯框架构建了一个新的因果模型，利用可识别因果方向的贝叶斯模型选择方法，在柔性模型类别中解决了数据集中的因果关系判断问题并且在各种生成数据假设下的性能优异。