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

Electrospun Stacked Dual-Channel Transistors with High Electron Mobility Using a Planar Heterojunction Architecture

Funding Information: This work was supported by National Natural Science Foundation of China (11774001) and Anhui Project (Z010118169). Publisher Copyright: © 2022 The Authors. Advanced Electronic Materials published by Wiley-VCH GmbH.Thin-film transistors based on metal oxide semiconductors have become a mainstream technology for application in driving low-cost backplanes of active matrix liquid crystal displays. Although significant progress has been made in traditional marketable devices based on physical vapor deposition derived metal oxides, it has still been hindered by low yield and poor compatibility. Fortunately, developing solution-based 1D nanofiber networks to act as the fundamental building blocks for transistor has proven to be a simpler, higher-throughput approach. However, oxide transistors based on such princesses suffer from degraded carrier mobility and operational instability, preventing the ability of such devices from replacing present polycrystalline Si technologies. Herein, it is shown that double channel heterojunction transistors with high electron mobility (>40 cm2 V−1 s−1) and operational stability can be achieved from electrospun double channels composed of In2O3 and ZnO layers. Adjusting the stacking order and the stacking density of In2O3 and ZnO layers can effectively optimize the interface electron trap, leading to the formation of 2D electron gas and the reduction of stress-induced instability. These findings further elucidate the significant advance of electrospinning-derived double channel heterojunction transistors toward practical applications for future low-cost and high-performance electronics.publishersversionpublishe

Multiple jets and γ\gamma-jet correlation in high-energy heavy-ion collisions

$\gamma$-jet production is considered one of the best probes of the hot quark-gluon plasma in high-energy heavy-ion collisions since the direct $\gamma$ can be used to gauge the initial energy and momentum of the associated jet. This is investigated within the Linear Boltzmann Transport (LBT) model for jet propagation and jet-induced medium excitation. With both parton energy loss and medium response from jet-medium interaction included, LBT can describe experimental data well on $\gamma$-jet correlation in Pb+Pb collisions at the Large Hadron Collider. Multiple jets associated with direct $\gamma$ production are found to contribute significantly to $\gamma$-jet correlation at small $p_T^{\rm jet}<p_T^\gamma$ and large azimuthal angle relative to the opposite direction of $\gamma$. Jet medium interaction not only suppresses the leading jet at large $p_T^{\rm jet}$ but also sub-leading jets at large azimuthal angle. This effectively leads to the narrowing of $\gamma$-jet correlation in azimuthal angle instead of broadening due to jet-medium interaction. The $\gamma$-jet profile on the other hand will be broadened due to jet-medium interaction and jet-induced medium response. Energy flow measurements relative to the direct photon is illustrated to reflect well the broadening and jet-induced medium response.Comment: 11 pages with 12 figures, revised version includes discussions on the background subtraction and different definitions of jet profil

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