Comment: Causal Inference in the Medical Area
英文摘要:
Comment on Causal Inference in the Medical Area [math.ST/0612783]
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Edward L. Korn
Comment on Causal Inference in the Medical Area [math.ST/0612783]
机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。
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Motivated by Breiman's rousing 2001 paper on the "two cultures" in statistics, we consider the role that different modeling approaches play in causal inference. We discuss the relationship between model complexity and causal (mis)interpretation, the relative merits of plug-in versus targeted estimation, issues that arise in tuning flexible estimators of causal effects, and some outstanding cultural divisions in causal inference.
CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images
该R包causalimages支持利用图像和图像序列数据进行因果推断,提供分解处理效应异质性、控制混杂因素及生成嵌入等功能,并包含加速大规模图像分析的设施,为R中因果推断开辟新能力。
Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data is available. In practice, causal networks are difficult to learn and interpret, and limited to relatively small datasets. We report a more reliable and scalable causal discovery method (iMIIC), based on a general mutual information supremum principle, which greatly improves the precision of inferred causal relations while distinguishing genuine causes from putative and latent causal effects. We showcase iMIIC on synthetic and real-life healthcare data from 396,179 breast …
(This comment has been updated to respond to Wang and Blei's rejoinder [arXiv:1910.07320].) The premise of the deconfounder method proposed in "Blessings of Multiple Causes" by Wang and Blei [arXiv:1805.06826], namely that a variable that renders multiple causes conditionally independent also controls for unmeasured multi-cause confounding, is incorrect. This can be seen by noting that no fact about the observed data alone can be informative about ignorability, since ignorability is compatible with any observed data distribution. Methods to control for unmeasured confounding may be valid with …
Causal Inference in Biomedical Imaging via Functional Linear Structural Equation Models
本文提出功能线性结构方程模型(FLSEM)用于医学影像中的因果推断,通过标量工具变量建立可识别条件,并开发FGS-DAR算法实现高效变量选择,验证了方法在检测因果关系方面的有效性。
Robust Bayesian causal estimation for causal inference in medical diagnosis
本文针对高维医学诊断中的因果效应估计问题,提出基于先验敏感性分析的谨慎贝叶斯组LASSO变量选择框架,并证明在信息有限时弃权选择或专家启发式选择可提升因果估计的稳定性。
Data collaboration for causal inference from limited medical testing and medication data
针对跨机构医疗数据因果推断的隐私挑战,研究提出DC-QE框架下的新型中间表示生成方法,实验验证其在IID与非IID场景下性能优于单机构分析、接近集中式分析,为医疗隐私保护因果推断提供可行方案。
We respond to comments on our paper, titled "Instrumental variable estimation of the causal hazard ratio."
Type 2 diabetes (T2DM), one of the most prevalent chronic diseases, affects the glucose metabolism of the human body, which decreases the quantity of life and brings a heavy burden on social medical care. Patients with T2DM are more likely to suffer bone fragility fracture as diabetes affects bone mineral density (BMD). However, the discovery of the determinant factors of BMD in a medical way is expensive and time-consuming. In this paper, we propose a novel algorithm, Prior-Knowledge-driven local Causal structure Learning (PKCL), to discover the underlying causal mechanism between BMD and its…
We introduce Targeted Smooth Bayesian Causal Forests (tsBCF), a nonparametric Bayesian approach for estimating heterogeneous treatment effects which vary smoothly over a single covariate in the observational data setting. The tsBCF method induces smoothness by parameterizing terminal tree nodes with smooth functions, and allows for separate regularization of treatment effects versus prognostic effect of control covariates. Smoothing parameters for prognostic and treatment effects can be chosen to reflect prior knowledge or tuned in a data-dependent way. We use tsBCF to analyze a new clinical p…
The aim of this comment (set to appear in a formal discussion in JASA) is to draw out some conclusions from an extended back-and-forth I have had with Wang and Blei regarding the deconfounder method proposed in "The Blessings of Multiple Causes" [arXiv:1805.06826]. I will make three points here. First, in my role as the critic in this conversation, I will summarize some arguments about the lack of causal identification in the bulk of settings where the "informal" message of the paper suggests that the deconfounder could be used. This is a point that is discussed at length in D'Amour 2019 [arXi…
There has recently been a growing interest in the development of statistical methods to compare medical costs between treatment groups. When cumulative cost is the outcome of interest, right-censoring poses the challenge of informative missingness due to heterogeneity in the rates of cost accumulation across subjects. Existing approaches seeking to address the challenge of informative cost trajectories typically rely on inverse probability weighting and target a net "intent-to-treat" effect. However, no approaches capable of handling time-dependent treatment and confounding in this setting hav…
Transfer Learning for Causal Effect Estimation
针对有限数据下的因果效应估计问题,提出迁移因果学习框架$\ell_1$-TCL,通过结合$\ell_1$正则化迁移学习与干扰模型估计,在高维稀疏环境下提供理论保证,并在模拟与真实医疗数据中展现出优于基线方法的性能。
Causal inference is widely used in various fields, such as biology, psychology and economics, etc. In observational studies, we need to balance the covariates before estimating causal effect. This study extends the one-dimensional entropy balancing method to multiple dimensions to balance the covariates. Both parametric and nonparametric methods are proposed to estimate the causal effect of multivariate continuous treatments and theoretical properties of the two estimations are provided. Furthermore, the simulation results show that the proposed method is better than other methods in various c…
Comment on Complex Causal Questions Require Careful Model Formulation: Discussion of Rubin on Experiments with ``Censoring'' Due to Death [math.ST/0612783]
Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference. Causal inference has the potential to have significant impact on medical research, prevention and control of diseases, and identifying factors that impact economic changes to name just a few. However, these promising applications for causal inference are often ones that involve sensitive or personal data of users that n…
A Causal Inference Approach for Evaluating Diagnostic Tests and AI-Enabled Medical Devices: From Effect Modification to Information-Augmented Decision-Making
本研究提出因果推理方法,区分诊断测试的解释性与实用性有效性,采用TMLE及交叉验证TMLE进行非参数估计,可用于评估诊断测试及人工智能医疗设备的作用途径。
The Regression Discontinuity Design in Medical Science
本文介绍断点回归设计在医学科学中的应用,重点阐述因果解释,并简要提及估计与推断的关键概念,附有医学实证示例。
We consider methods for causal inference in randomized trials nested within cohorts of trial-eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite-sample performance in simulation studies. As an illustration, we apply the esti…
Adjusting for indirectly measured confounding using large-scale propensity scores
本文针对观测数据中的间接测量混杂问题,研究了大规模倾向得分(LSPS)方法在医疗因果分析中的表现,证明其能通过纳入数万协变量有效调整间接混杂并避免碰撞器等引发的偏差。
High Dimensional Causal Inference with Variational Backdoor Adjustment
本文提出一种基于生成建模和变分推断的后门调整方法,用于高维治疗和混杂因素下的因果效应估计,并在半合成X射线医学数据等场景中验证了其有效性。
Time-Varying Causal Survival Learning
本研究填补交错采纳设计与生存分析的空白,结合双重机器学习方法估计时变处理的因果效应,有效减少偏差并提升效率。
Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer
本研究探索利用大语言模型预测非小细胞肺癌因果图中边的方向性,在去标识化患者数据上验证,结果表明其性能优于现有最先进方法,助力医学因果发现。
Heterogeneity in medical data, e.g., from data collected at different sites and with different protocols in a clinical study, is a fundamental hurdle for accurate prediction using machine learning models, as such models often fail to generalize well. This paper leverages a recently proposed normalizing-flow-based method to perform counterfactual inference upon a structural causal model (SCM), in order to achieve harmonization of such data. A causal model is used to model observed effects (brain magnetic resonance imaging data) that result from known confounders (site, gender and age) and exoge…
In medical practice, treatments are selected based on the expected causal effects on patient outcomes. Here, the gold standard for estimating causal effects are randomized controlled trials; however, such trials are costly and sometimes even unethical. Instead, medical practice is increasingly interested in estimating causal effects among patient (sub)groups from electronic health records, that is, observational data. In this paper, we aim at estimating the average causal effect (ACE) from observational data (patient trajectories) that are collected over time. For this, we propose DeepACE: an …
Visual Analysis of Multi-outcome Causal Graphs
本文提出多结果因果图的可视化方法,用于比较不同结果变量的因果图。通过与医学专家合作,设计了两种可视化技术,用于比较多种因果发现算法,并通过案例研究和用户测试验证方法有效性。
Hahn et al. (2020) offers an extensive study to explicate and evaluate the performance of the BCF model in different settings and provides a detailed discussion about its utility in causal inference. It is a welcomed addition to the causal machine learning literature. I will emphasize the contribution of the BCF model to the field of causal inference through discussions on two topics: 1) the difference between the PS in the BCF model and the Bayesian PS in a Bayesian updating approach, 2) an alternative exposition of the role of the PS in outcome modeling based methods for the estimation of ca…
Functional Causal Inference with Time-to-Event Data
针对函数型预测变量与时间至事件结局间的因果推断难题,提出含FAFT模型及回归调整、逆概率加权、双重稳健方法的框架,并通过模拟与ADNI数据验证其稳健性及医学价值。
CafeMed: Causal Attention Fusion Enhanced Medication Recommendation
针对现有药物推荐方法未建模医疗实体协同效应、静态因果关系无法适配患者个体状态的问题,本文提出融合动态因果推理与跨模态注意力的CafeMed框架,在MIMIC系列数据集上取得了更优的推荐准确率与更低的药物相互作用率。
Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury
本文提出结合目标试验模拟与机器学习的因果模型,用于估计药物导致不良事件的因果概率下界,并在万古霉素相关急性肾损伤场景中与专家评估比较。
Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data
本文针对乳腺癌生存预测中因果发现评估困难的问题,利用PC、GES和广义精度矩阵算法分析705名患者的多组学数据,并结合BlueBERT等生物医学语言模型验证结果,以揭示可靠的临床因果关系。
Detecting clinician implicit biases in diagnoses using proximal causal inference
该研究提出一种基于近端因果推断的方法,通过近端中介分析患者社会人口学属性对临床医生诊断决策的路径特定效应,利用UK Biobank真实数据检测大规模医疗数据中临床医生隐性偏见对患者结果的影响。
Causal or unconfounded descriptive comparisons between multiple groups are common in observational studies. Motivated from a racial disparity study in health services research, we propose a unified propensity score weighting framework, the balancing weights, for estimating causal effects with multiple treatments. These weights incorporate the generalized propensity scores to balance the weighted covariate distribution of each treatment group, all weighted toward a common pre-specified target population. The class of balancing weights include several existing approaches such as the inverse prob…
Moments of Causal Effects
本文提出因果效应矩和乘积矩的定义、识别定理与界,从分布和关系角度拓展传统平均因果效应分析,并通过模拟和真实医学数据验证其估计与应用价值。
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