In the investigation of any causal mechanisms, such as the brain's causal
networks, the assumption of causal sufficiency plays a critical role. Notably,
neglecting this assumption can result in significant errors, a fact that is
often disregarded in the causal analysis of brain networks. In this study, we
propose an algorithmic identification approach for determining essential
exogenous nodes that satisfy the critical need for causal sufficiency to adhere
to it in such inquiries. Our approach consists of three main steps: First, by
capturing the essence of the Peter-Clark (PC) algorithm, we conduct
independence tests for pairs of regions within a network, as well as for the
same pairs conditioned on nodes from other networks. Next, we distinguish
candidate confounders by analyzing the differences between the conditional and
unconditional results, using the Kolmogorov-Smirnov test. Subsequently, we
utilize Non-Factorized identifiable Variational Autoencoders (NF-iVAE) along
with the Correlation Coefficient index (CCI) metric to identify the confounding
variables within these candidate nodes. Applying our method to the Human
Connectome Projects (HCP) movie-watching task data, we demonstrate that while
interactions exist between dorsal and ventral regions, only dorsal regions
serve as confounders for the visual networks, and vice versa. These findings
align consistently with those resulting from the neuroscientific perspective.
Finally, we show the reliability of our results by testing 30 independent runs
for NF-iVAE initialization.

利用算法鉴别外源节点以满足因果充分性的需求，通过独立性测试和差异分析确定可能的混淆变量，应用于人类连通性计划数据并验证了该方法的可靠性。

脑网络中因果充分性的必要外部节点的算法鉴定

Algorithmic Identification of Essential Exogenous Nodes for Causal  Sufficiency in Brain Networks

Healthcare data often come from multiple sites in which the correlations
between confounding variables can vary widely. If deep learning models exploit
these unstable correlations, they might fail catastrophically in unseen sites.
Although many methods have been proposed to tackle unstable correlations, each
has its limitations. For example, adversarial training forces models to
completely ignore unstable correlations, but doing so may lead to poor
predictive performance. Other methods (e.g. Invariant risk minimization [4])
try to learn domain-invariant representations that rely only on stable
associations by assuming a causal data-generating process (input X causes class
label Y ). Thus, they may be ineffective for anti-causal tasks (Y causes X),
which are common in computer vision. We propose a method called CoPA
(Conditional Prevalence-Adjustment) for anti-causal tasks. CoPA assumes that
(1) generation mechanism is stable, i.e. label Y and confounding variable(s) Z
generate X, and (2) the unstable conditional prevalence in each site E fully
accounts for the unstable correlations between X and Y . Our crucial
observation is that confounding variables are routinely recorded in healthcare
settings and the prevalence can be readily estimated, for example, from a set
of (Y, Z) samples (no need for corresponding samples of X). CoPA can work even
if there is a single training site, a scenario which is often overlooked by
existing methods. Our experiments on synthetic and real data show CoPA beating
competitive baselines.

CoPA 是一种用于反因果任务的方法，假设生成机制稳定，即标签 Y 和混淆变量 Z 生成 X，而不稳定的条件普遍性完全解释了 X 和 Y 之间的不稳定相关性。该方法在合成数据和真实数据上击败了竞争基准线。

通过条件性患病率调整实现鲁棒学习

Robust Learning via Conditional Prevalence Adjustment

Many modern applications seek to understand the relationship between an
outcome variable $Y$ and a covariate $X$ in the presence of a (possibly
high-dimensional) confounding variable $Z$. Although much attention has been
paid to testing \emph{whether} $Y$ depends on $X$ given $Z$, in this paper we
seek to go beyond testing by inferring the \emph{strength} of that dependence.
We first define our estimand, the minimum mean squared error (mMSE) gap, which
quantifies the conditional relationship between $Y$ and $X$ in a way that is
deterministic, model-free, interpretable, and sensitive to nonlinearities and
interactions. We then propose a new inferential approach called
\emph{floodgate} that can leverage any working regression function chosen by
the user (allowing, e.g., it to be fitted by a state-of-the-art machine
learning algorithm or be derived from qualitative domain knowledge) to
construct asymptotic confidence bounds, and we apply it to the mMSE gap.
\acc{We additionally show that floodgate's accuracy (distance from confidence
bound to estimand) is adaptive to the error of the working regression
function.} We then show we can apply the same floodgate principle to a
different measure of variable importance when $Y$ is binary. Finally, we
demonstrate floodgate's performance in a series of simulations and apply it to
data from the UK Biobank to infer the strengths of dependence of platelet count
on various groups of genetic mutations.

采用最小均方误差 (mMSE) 间隙和 floodgate 方法，推断出变量间的条件关系（包括非线性和交互作用），构建渐近置信区间，并应用于不同测量下的变量重要性和基因突变的血小板计数的相关性推断。

Floodgate: 无模型变量重要性推断

Floodgate: inference for model-free variable importance

The desire to train complex machine learning algorithms and to increase the
statistical power in association studies drives neuroimaging research to use
ever-larger datasets. The most obvious way to increase sample size is by
pooling scans from independent studies. However, simple pooling is often
ill-advised as selection, measurement, and confounding biases may creep in and
yield spurious correlations. In this work, we combine 35,320 magnetic resonance
images of the brain from 17 studies to examine bias in neuroimaging. In the
first experiment, Name That Dataset, we provide empirical evidence for the
presence of bias by showing that scans can be correctly assigned to their
respective dataset with 71.5% accuracy. Given such evidence, we take a closer
look at confounding bias, which is often viewed as the main shortcoming in
observational studies. In practice, we neither know all potential confounders
nor do we have data on them. Hence, we model confounders as unknown, latent
variables. Kolmogorov complexity is then used to decide whether the confounded
or the causal model provides the simplest factorization of the graphical model.
Finally, we present methods for dataset harmonization and study their ability
to remove bias in imaging features. In particular, we propose an extension of
the recently introduced ComBat algorithm to control for global variation across
image features, inspired by adjusting for population stratification in
genetics. Our results demonstrate that harmonization can reduce
dataset-specific information in image features. Further, confounding bias can
be reduced and even turned into a causal relationship. However, harmonziation
also requires caution as it can easily remove relevant subject-specific
information. Code is available at this https URL

本研究探讨了在神经影像学中处理数据集偏差的方法，通过结合 17 项研究的 35320 个磁共振成像脑图像来研究偏差，并提出数据集谐波化的方法以消除偏差及控制混淆因素。