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合成工具变量：从稀疏关联到稀疏因果效应AI 解读

Abstract In many observational studies, researchers are often interested in the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data, such as the Lasso, assume that the associations between the exposures and the outcome are sparse. However, these methods do not estimate causal effects in the presence of unmeasured confounding. In this paper, we consider an alternative approach that assumes the causal effects under consideration are…

赵睿轩等对《组合回归》的讨论意见AI 解读

Ruixuan Zhao, Oliver Dukes, Linbo Wang, Lin Liu; Ruixuan Zhao, Oliver Dukes, Linbo Wang, and Lin Liu’s contribution to the Discussion of ‘Regression by com

严景新、刘林等对《组合回归》的讨论AI 解读

Jingxin Yan, Lin Liu, Oliver Dukes, Qizhai Li, Linbo Wang; Jingxin Yan, Lin Liu, Oliver Dukes, Qizhai Li, and Linbo Wang’s contribution to the Discussion o

董梅、王林波、刘林与Oliver Dukes对“复合回归”的讨论意见AI 解读

Mei Dong, Linbo Wang, Lin Liu, Oliver Dukes; Mei Dong, Linbo Wang, Lin Liu, and Oliver Dukes's contribution to the Discussion of ‘Regression by composition

平行结局的潜力AI 解读

Summary A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we introduce a novel approach for causal inference that leverages information in multiple outcomes to deal with unmeasured confounding. An important assumption in our approach is conditional independence among multiple outcomes. In contrast to existing proposals in the literature, the roles of…

以分布函数为结果的因果推断AI 解读

Abstract Understanding causal relationships is one of the most important goals of modern science. So far, the causal inference literature has focused almost exclusively on outcomes coming from the Euclidean space Rp. However, it is increasingly common that complex datasets are best summarized as data points in nonlinear spaces. In this paper, we present a novel framework of causal effects for outcomes from the Wasserstein space of cumulative distribution functions, which in…

多重处理与二元结果下因果效应的可识别性AI 解读

Summary Unobserved confounding presents a major threat to causal inference in observational studies. Recently, several authors have suggested that this problem could be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that under a linear Gaussian model for the treatments, the causal effect is not identifiable without parametric assumptions on the outcome model. In this note, we show that…

二元工具变量模型下局部处理效应的估计AI 解读

Summary Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges of causal estimation with a binary outcome and…

利用工具变量进行有界、高效且多重稳健的平均处理效应估计AI 解读

Summary Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. Under the standard instrumental variable model, however, the average treatment effect is only partially identifiable. To address this, we propose novel assumptions that enable identification of the average treatment effect. Our identification assumptions are clearly separated from model assumptions that are needed for estimation, so researchers are not…

死亡截断结局下因果效应的识别与估计AI 解读

It is common in medical studies that the outcome of interest is truncated by death, meaning that a subject has died before the outcome could be measured. In this case, restricted analysis among survivors may be subject to selection bias. Hence, it is of interest to estimate the survivor average causal effect, defined as the average causal effect among the subgroup consisting of subjects who would survive under either exposure. In this paper, we consider the identification…

死亡截断下有序处理与二元结局的因果分析AI 解读

Summary It is common that, in multiarm randomized trials, the outcome of interest is ‘truncated by death’, meaning that it is only observed or well-defined conditioning on an intermediate outcome. In this case, in addition to pairwise contrasts, the joint inference for all treatment arms is also of interest. Under a monotonicity assumption we present methods for both pairwise and joint causal analyses of ordinal treatments and binary outcomes in the presence of truncation by…

Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. The discrete instrumental variable model has testable implications for the law of the observed data. However, current assessments of instrumental validity are typically based solely on subject-matter arguments rather than these testable implications, partly due to a lack of formal statistical tests with known properties. In this paper, we develop simple procedures…