日期精度：到日 13 篇 · 到月 1 篇 · 到年 0 篇
作者对《作为线性回归的增广平衡权重》讨论的回复
David Bruns-Smith, Oliver Dukes, Avi Feller, Elizabeth L Ogburn; Authors’ reply to the Discussion of ‘Augmented balancing weights as linear regression' by
作为线性回归的增广平衡权重
Abstract We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights—weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single…
面向政策制定者的处理效应同质性非参数检验
Recent work has focused on nonparametric estimation of conditional treatment effects, but inference has remained relatively unexplored. We propose a class of nonparametric tests for both quantitative and qualitative treatment effect heterogeneity. The tests can incorporate a variety of structured assumptions on the conditional average treatment effect, allow for both continuous and discrete covariates, and do not require sample splitting to obtain a tractable asymptotic null…
赵睿轩等对《组合回归》的讨论意见
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
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
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
从历史安慰剂对照试验推广活性对照的意向治疗效应：HPTN 084 案例
In many clinical settings, an active-controlled trial design (e.g., a non-inferiority or superiority design) is often used to compare an experimental medicine to an active control (e.g., an FDA-approved, standard therapy). One prominent example is a recent phase 3 efficacy trial, HIV Prevention Trials Network Study 084 (HPTN 084), comparing long-acting cabotegravir, a new HIV pre-exposure prophylaxis (PrEP) agent, to the FDA-approved daily oral tenofovir disoproxil fumarate…
协调 model-X 与双重稳健条件独立检验
Model-X approaches to testing conditional independence between a predictor and an outcome variable given a vector of covariates usually assume exact knowledge of the conditional distribution of the predictor given the covariates. Nevertheless, model-X methodologies are often deployed with this conditional distribution learned in sample. We investigate the consequences of this choice through the lens of the distilled conditional randomization test (dCRT). We find that Type-I…
弱模型假设下的Cox回归
Inference for the conditional association between an exposure and a time-to-event endpoint, given covariates, is routinely based on partial likelihood estimators for hazard ratios indexing Cox proportional hazards models. This approach is flexible and makes testing straightforward, but is nonetheless not entirely satisfactory. First, there is no good understanding of what it infers when the model is misspecified. Second, it is common to employ variable selection procedures…
近端中介分析
Summary A common concern when trying to draw causal inferences from observational data is that the measured covariates are insufficiently rich to account for all sources of confounding. In practice, many of the covariates may only be proxies of the latent confounding mechanism. Recent work has shown that in certain settings where the standard no-unmeasured-confounding assumption fails, proxy variables can be leveraged to identify causal effects. Results currently exist for…
广义线性模型参数的弱假设推断
Abstract Inference for the parameters indexing generalised linear models is routinely based on the assumption that the model is correct and a priori specified. This is unsatisfactory because the chosen model is usually the result of a data-adaptive model selection process, which may induce excess uncertainty that is not usually acknowledged. Moreover, the assumptions encoded in the chosen model rarely represent some a priori known, ground truth, making standard inferences…
可能误设的高维模型中处理效应参数的推断
Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates…
对 Kallus 及 Mo、Qi、Liu 的讨论：政策学习的新目标
One of the key contributions that the causal inference literature has brought is a focus on clearly defined estimands. This focus has led to a real shift in the statistical approach (see, e.g., Dan...