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作者：Lee, A.; Whiteley, N.
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作者：Wang, Y. Samuel; Drton, Mathias
作者单位：University of Chicago; Technical University of Munich
摘要：We consider graphical models based on a recursive system of linear structural equations. This implies that there is an ordering, sigma, of the variables such that each observed variable Y-v is a linear function of a variable-specific error term and the other observed variables Y-u with sigma(u) < sigma(v). The causal relationships, i.e., which other variables the linear functions depend on, can be described using a directed graph. It has previously been shown that when the variable-specific er...
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作者：Yang, S.; Pieper, K.; Cools, F.
作者单位：North Carolina State University; Duke University
摘要：Structural failure time models are causal models for estimating the effect of time-varying treatments on a survival outcome. G-estimation and artificial censoring have been proposed for estimating the model parameters in the presence of time-dependent confounding and administrative censoring. However, most existing methods require manually pre-processing data into regularly spaced data, which may invalidate the subsequent causal analysis. Moreover, the computation and inference are challenging...
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作者：Wang, Xuan; Parast, Layla; Tian, Lu; Cai, Tianxi
作者单位：Zhejiang University; RAND Corporation; Stanford University; Harvard University
摘要：In randomized clinical trials, the primary outcome, Y, often requires long-term follow-up and/or is costly to measure. For such settings, it is desirable to use a surrogate marker, S, to infer the treatment effect on Y, Delta. Identifying such an S and quantifying the proportion of treatment effect on Y explained by the effect on S are thus of great importance. Most existing methods for quantifying the proportion of treatment effect are model based and may yield biased estimates under model mi...
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作者：Lee, Jarod Y. L.; Green, Peter J.; Ryan, Louise M.
作者单位：University of Technology Sydney
摘要：This article concerns a class of generalized linear mixed models for two-level grouped data, where the random effects are uniquely indexed by groups and are independent. We derive necessary and sufficient conditions for the marginal likelihood to be expressed in explicit form. These models are unified under the conjugate generalized linear mixed models framework, where conjugate refers to the fact that the marginal likelihood can be expressed in closed form, rather than implying inference via ...
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作者：Jiang, Zhichao; Ding, Peng
作者单位：University of Massachusetts System; University of Massachusetts Amherst; University of California System; University of California Berkeley
摘要：Instrumental variable methods can identify causal effects even when the treatment and outcome are confounded. We study the problem of imperfect measurements of the binary instrumental variable, treatment and outcome. We first consider nondifferential measurement errors, that is, the mismeasured variable does not depend on other variables given its true value. We show that the measurement error of the instrumental variable does not bias the estimate, that the measurement error of the treatment ...
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作者：Dunson, D. B.; Johndrow, J. E.
作者单位：Duke University; University of Pennsylvania
摘要：In a 1970 Biometrika paper, W. K. Hastings developed a broad class of Markov chain algorithms for sampling from probability distributions that are difficult to sample from directly. The algorithm draws a candidate value from a proposal distribution and accepts the candidate with a probability that can be computed using only the unnormalized density of the target distribution, allowing one to sample from distributions known only up to a constant of proportionality. The stationary distribution o...
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作者：Wang, Yixin; Zubizarreta, Jose R.
作者单位：Columbia University; Harvard University
摘要：Weighting methods are widely used to adjust for covariates in observational studies, sample surveys, and regression settings. In this paper, we study a class of recently proposed weighting methods, which find the weights of minimum dispersion that approximately balance the covariates. We call these weights 'minimal weights' and study them under a common optimization framework. Our key observation is that finding weights which achieve approximate covariate balance is equivalent to performing sh...
