The effect of vigorous physical activity on mortality in the elderly is difficult to estimate using conventional approaches to causal inference that define this effect by comparing the mortality risks corresponding to hypothetical scenarios in which all subjects in the target population engage in a given level of vigorous physical activity. A causal effect defined on the basis of such a static treatment intervention can only be identified from observed data if all subjects in the target population have a positive probability of selecting each of the candidate treatment options, an assumption that is highly unrealistic in this case since subjects with serious health problems will not be able to engage in higher levels of vigorous physical activity. This problem can be addressed by focusing instead on causal effects that are defined on the basis of realistic individualized treatment rules and intention-to-treat rules that explicitly take into account the set of treatment options that are available to each subject. We present a data analysis to illustrate that estimators of static causal effects in fact tend to overestimate the beneficial impact of high levels of vigorous physical activity while corresponding estimators based on realistic individualized treatment rules and intention-to-treat rules can yield unbiased estimates. We emphasize that the problems encountered in estimating static causal effects are not restricted to the IPTW estimator, but are also observed with the $G$-computation estimator, the DR-IPTW estimator, and the targeted MLE. Our analyses based on realistic individualized treatment rules and intention-to-treat rules suggest that high levels of vigorous physical activity may confer reductions in mortality risk on the order of 15-30%, although in most cases the evidence for such an effect does not quite reach the 0.05 level of significance.
Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation
Thurgau Institute for Digital Transformation (TIDIT)（图尔豪数字转型研究所）；HTWG Konstanz – University of Applied Sciences Konstanz（康斯坦茨应用科学大学）；Department of Neurology, University Hospital Zurich (USZ)（苏黎世大学神经科部）；University of Zurich (UZH)（苏黎世大学）
Using Individualized Treatment Effects to Assess Treatment Effect Heterogeneity
Advanced Methodology and Data Science, Novartis Pharma AG（诺华制药AG高级方法与数据科学）；China Novartis Institutes for Bio-medical Research CO.（中国诺华生物医学研究院有限公司）；Advanced Methodology and Data Science, Novartis Pharmaceuticals Corporation（诺华制药有限公司高级方法与数据科学）；Department of Statistics, Stanford University（斯坦福大学统计学系）
Individualized Dynamic Mediation Analysis Using Latent Factor Models
University of Pennsylvania（宾夕法尼亚大学）；The Pennsylvania State University（宾夕法尼亚州立大学）；The University of Texas at San Antonio（圣安东尼奥德克萨斯大学）；Fudan University（复旦大学）；University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）
Causal machine learning for predicting treatment outcomes
LMU Munich（慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Cambridge（剑桥大学）；TU Munich（慕尼黑工业大学）；Helmholtz Munich（慕尼黑亥姆霍兹中心）；Harvard Medical School（哈佛医学院）；Cambridge Centre for AI in Medicine（剑桥人工智能医学中心）；The Alan Turing Institute（阿兰·图灵研究所）
Causal mediation analysis for zero-inflated longitudinal data in the presence of treatment non-compliance and multiple mediators
University of Chicago（芝加哥大学）；Smeal College of Business, The Pennsylvania State University（宾夕法尼亚州立大学斯米尔商学院）；University of Florida（佛罗里达大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
Generative Flows for Potential Outcomes and Counterfactuals
School of Industrial and Systems Engineering（工业与系统工程学院）；Georgia Institute of Technology（佐治亚理工学院）；Elmore Family School of Electrical and Computer Engineering（埃洛姆家族电气与计算机工程学院）；Purdue University（普渡大学）
Quantifying uncertainty of individualized treatment effects in right-censored survival data: A comparison of Bayesian additive regression trees and causal survival forest
Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference
University of California, Berkeley（加州大学伯克利分校）；Carnegie Mellon University（卡内基梅隆大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Texas, Austin（德克萨斯大学奥斯汀分校）
Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury
Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；Amsterdam Public Health（阿姆斯特丹公共卫生研究所）；Institute for Logic, Language and Computation（逻辑、语言与计算研究所）；Pacmed（Pacmed公司）
Closing the problem of which causal structures of up to six total nodes have a classical-quantum gap
Department of Mathematics, University of York（约克大学数学系）；Aix-Marseille University, CNRS, LIS（阿维尼翁-马赛大学，法国国家科学研究中心，LIS）；Department of Mathematics, King's College London（伦敦国王学院数学系）
