在联合分析中使用机器学习检验因果假设

Causal Structural Hypothesis Testing and Data Generation Models

A vast amount of expert and domain knowledge is captured by causal structural priors, yet there has been little research on testing such priors for generalization and data synthesis purposes. We propose a novel model architecture, Causal Structural Hypothesis Testing, that can use nonparametric, structural causal knowledge and approximate a causal model's functional relationships using deep neural networks. We use these architectures for comparing structural priors, akin to hypothesis testing, using a deliberate (non-random) split of training and testing data. Extensive simulations demonstrate…

在加权FDR调整的p值指导下与领域专家共同开发因果图

Co-Developing Causal Graphs with Domain Experts Guided by Weighted FDR-Adjusted p-values

本文提出一种结合领域专家与统计建模者共同设计因果图的迭代方法，利用FDCR调整的p值作为反馈机制，指导因果图的修改与完善，最终实现融合专家知识与数据证据的稳健因果结构构建。

将治疗反应作为潜变量

Treatment response as a latent variable

针对区分治疗反应者与非反应者的统计推断问题，提出因果两组（C2G）模型及半参数、完全非参数两种经验贝叶斯方法，控制错误发现率且具近最优功效，在癌症免疫治疗数据集上恢复临床验证的预测生物标志物。

在全基因组研究中利用条件对称多维高斯混合模型检验大量复合原假设

Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies

本文提出条件对称多维高斯混合模型（csmGmm），用于统一遗传研究中中介、多效性和重复性分析的复合原假设检验，并证明其能使lfdr与z统计量检验规则保持一致，在肺癌遗传关联数据上验证了方法的稳健性和可解释性。

Bayesian Mendelian randomization testing of interval causal null hypotheses: ternary decision rules and loss function calibration

Our approach to Mendelian Randomization (MR) analysis is designed to increase reproducibility of causal effect "discoveries" by: (i) using a Bayesian approach to inference; (ii) replacing the point null hypothesis with a region of practical equivalence consisting of values of negligible magnitude for the effect of interest, while exploiting the ability of Bayesian analysis to quantify the evidence of the effect falling inside/outside the region; (iii) rejecting the usual binary decision logic in favour of a ternary logic where the hypothesis test may result in either an acceptance or a rejecti…

事件链图的因果操作

The causal manipulation of chain event graphs

离散贝叶斯网络作为推理和表达某些因果假设的框架已经非常成功。在本文中，我们提出了一类图形模型称为链事件图（CEG）模型，推广了离散BN模型的类。它提供了一个灵活和富有表现力的框架，用于表示和分析因果假设的含义，表达在生成的基础系统的操纵的影响方面。我们证明，作为BN，因果效应的可识别性分析可以通过检查CEG图的拓扑结构来进行，从而导致类似于BN的后门定理的定理。

On null hypotheses in survival analysis

The conventional nonparametric tests in survival analysis, such as the log-rank test, assess the null hypothesis that the hazards are equal at all times. However, hazards are hard to interpret causally, and other null hypotheses are more relevant in many scenarios with survival outcomes. To allow for a wider range of null hypotheses, we present a generic approach to define test statistics. This approach utilizes the fact that a wide range of common parameters in survival analysis can be expressed as solutions of differential equations. Thereby we can test hypotheses based on survival parameter…

Gaussian Prepivoting for Finite Population Causal Inference

In finite population causal inference exact randomization tests can be constructed for sharp null hypotheses, i.e. hypotheses which fully impute the missing potential outcomes. Oftentimes inference is instead desired for the weak null that the sample average of the treatment effects takes on a particular value while leaving the subject-specific treatment effects unspecified. Without proper care, tests valid for sharp null hypotheses may be anti-conservative should only the weak null hold, creating the risk of misinterpretation when randomization tests are deployed in practice. We develop a gen…

关于单位间干扰的推理

Reasoning about Interference Between Units

如果一个实验性的治疗是由治疗组和对照组的单位，测试的因果关系的假设可能很难概念化，更不用说执行。在本文中，我们将展示如何反事实因果模型可能会被写入和测试时，理论表明溢出或其他基于网络的实验单位之间的干扰。我们表明，“无干扰”的假设不需要约束学者谁有有趣的问题干扰。我们为研究人员提供了建模理论的能力，这些理论是关于某些单位的治疗如何影响其他单位的结果的。我们进一步展示了如何检验这些因果效应的假设，我们提供的工具，使研究人员能够评估他们的测试给自己的模型，设计，检验统计量和数据的操作特性。我们在这里开发的概念和方法框架特别适用于社交网络，但当研究人员想知道单元之间的干扰时，可以有效地部署。单元之间的干扰不一定是一个不可检验的假设;相反，干扰是一个机会，可以对理论上有趣的现象提出有意义的问题。

A Graph-Theoretic Approach to Randomization Tests of Causal Effects Under General Interference

Interference exists when a unit's outcome depends on another unit's treatment assignment. For example, intensive policing on one street could have a spillover effect on neighboring streets. Classical randomization tests typically break down in this setting because many null hypotheses of interest are no longer sharp under interference. A promising alternative is to instead construct a conditional randomization test on a subset of units and assignments for which a given null hypothesis is sharp. Finding these subsets is challenging, however, and existing methods are limited to special cases or …

基于参数化知识的上下文感知推理用于推断因果变量

Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables

本文提出一个包含4000多个查询的因果图补全基准，证明LLM能根据全图上下文推理后门变量，而非简单记忆关联。

A robust approach to quantifying uncertainty in matching problems of causal inference

Unquantified sources of uncertainty in observational causal analyses can break the integrity of the results. One would never want another analyst to repeat a calculation with the same dataset, using a seemingly identical procedure, only to find a different conclusion. However, as we show in this work, there is a typical source of uncertainty that is essentially never considered in observational causal studies: the choice of match assignment for matched groups, that is, which unit is matched to which other unit before a hypothesis test is conducted. The choice of match assignment is anything bu…

任意时间有效的线性模型与随机对照试验中的回归调整因果推断

Anytime-Valid Linear Models and Regression Adjusted Causal Inference in Randomized Experiments

本文提出任意时间有效的线性模型推断方法，通过序列化检验和置信集实现统一的误差控制，结合贝叶斯和频数派最优性，用于随机对照试验中的因果推断，提升A/B测试的实用性。