Identifying Causal Effects Using Instrumental Variables from the Auxiliary Population

Instrumental variable approaches have gained popularity for estimating causal effects in the presence of unmeasured confounding. However, the availability of instrumental variables in the primary population is often challenged due to stringent and untestable assumptions. This paper presents a novel method to identify and estimate causal effects in the primary population by utilizing instrumental variables from the auxiliary population, incorporating a structural equation model, even in scenarios with nonlinear treatment effects. Our approach involves using two datasets: one from the primary population with joint observations of treatment and outcome, and another from the auxiliary population providing information about the instrument and treatment. Our strategy differs from most existing methods by not depending on the simultaneous measurements of instrument and outcome. The central idea for identifying causal effects is to establish a valid substitute through the auxiliary population, addressing unmeasured confounding. This is achieved by developing a control function and projecting it onto the function space spanned by the treatment variable. We then propose a three-step estimator for estimating causal effects and derive its asymptotic results. We illustrate the proposed estimator through simulation studies, and the results demonstrate favorable performance. We also conduct a real data analysis to evaluate the causal effect between vitamin D status and BMI.Comment: 19 page

Mediation pathway selection with unmeasured mediator-outcome confounding

Causal mediation analysis aims to investigate how an intermediary factor, called a mediator, regulates the causal effect of a treatment on an outcome. With the increasing availability of measurements on a large number of potential mediators, methods for selecting important mediators have been proposed. However, these methods often assume the absence of unmeasured mediator-outcome confounding. We allow for such confounding in a linear structural equation model for the outcome and further propose an approach to tackle the mediator selection issue. To achieve this, we firstly identify causal parameters by constructing a pseudo proxy variable for unmeasured confounding. Leveraging this proxy variable, we propose a partially penalized method to identify mediators affecting the outcome. The resultant estimates are consistent, and the estimates of nonzero parameters are asymptotically normal. Motivated by these results, we introduce a two-step procedure to consistently select active mediation pathways, eliminating the need to test composite null hypotheses for each mediator that are commonly required by traditional methods. Simulation studies demonstrate the superior performance of our approach compared to existing methods. Finally, we apply our approach to genomic data, identifying gene expressions that potentially mediate the impact of a genetic variant on mouse obesity.Comment: 35 page

Identification and Estimation of Causal Effects Using non-Gaussianity and Auxiliary Covariates

Assessing causal effects in the presence of unmeasured confounding is a challenging problem. Although auxiliary variables, such as instrumental variables, are commonly used to identify causal effects, they are often unavailable in practice due to stringent and untestable conditions. To address this issue, previous researches have utilized linear structural equation models to show that the causal effect can be identifiable when noise variables of the treatment and outcome are both non-Gaussian. In this paper, we investigate the problem of identifying the causal effect using auxiliary covariates and non-Gaussianity from the treatment. Our key idea is to characterize the impact of unmeasured confounders using an observed covariate, assuming they are all Gaussian. The auxiliary covariate can be an invalid instrument or an invalid proxy variable. We demonstrate that the causal effect can be identified using this measured covariate, even when the only source of non-Gaussianity comes from the treatment. We then extend the identification results to the multi-treatment setting and provide sufficient conditions for identification. Based on our identification results, we propose a simple and efficient procedure for calculating causal effects and show the $\sqrt{n}$-consistency of the proposed estimator. Finally, we evaluate the performance of our estimator through simulation studies and an application.Comment: 16 papges, 7 Figure

(R)-[1-(2-Chloro­phen­yl)-2-meth­oxy-2-oxoeth­yl][2-(thio­phen-2-yl)eth­yl]ammonium (+)-camphor-10-sulfonate acetone monosolvate

The title compound, C15H17ClNO2S+·C10H15O4S−·C3H6O, was synthesized by N-alkyl­ation of α-amino-(2-chloro­phen­yl)acetate with 2-thienylethyl p-toluene­sulfonate, followed by reaction with (+)-camphor-10-sulfonic acid. In the crystal, the cations and anions are linked through N—H⋯O hydrogen bonds. The thio­phene ring of the cation was found to be disordered over two sites, with refined occupancies of 0.798 (4) and 0.202 (4)

Optimization calculation of stope structure parameters based on Mathews stabilization graph method

Mathews stability graphic method, based on the rock classification system, measures the stability of the ore roof area of a relatively simple calculation method and provides a theoretical basis for mine rational design stope structure size parameters. In this study, we used a large-scale tungsten mine in Jiangxi Province as the engineering background and performed on-site engineering geological surveys and indoor ore rock mechanics tests in the middle section of mine 417 to obtain multiple engineering quality indicators for the mines and surrounding rocks. The Mathews stability map method and Barton limit span theory were used. The reasonable size range of the exposed face of the stope was calculated by performing theoretical analysis on the ultimate span. Then, FLAC3D calculation and analysis software were used for the simulation of the stope structure, and the most reasonable design of the exposed surface dimension was selected and used as reference for ensuring the safe production of the mine