62 research outputs found
Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?
The Potential Outcome Framework (POF) plays a prominent role in the field of
causal inference. Most causal inference models based on the POF (CIMs-POF) are
designed for eliminating confounding bias and default to an underlying
assumption of Confounding Covariates. This assumption posits that the
covariates consist solely of confounders. However, the assumption of
Confounding Covariates is challenging to maintain in practice, particularly
when dealing with high-dimensional covariates. While certain methods have been
proposed to differentiate the distinct components of covariates prior to
conducting causal inference, the consequences of treating non-confounding
covariates as confounders remain unclear. This ambiguity poses a potential risk
when conducting causal inference in practical scenarios. In this paper, we
present a unified graphical framework for the CIMs-POF, which greatly enhances
the comprehension of these models' underlying principles. Using this graphical
framework, we quantitatively analyze the extent to which the inference
performance of CIMs-POF is influenced when incorporating various types of
non-confounding covariates, such as instrumental variables, mediators,
colliders, and adjustment variables. The key findings are: in the task of
eliminating confounding bias, the optimal scenario is for the covariates to
exclusively encompass confounders; in the subsequent task of inferring
counterfactual outcomes, the adjustment variables contribute to more accurate
inferences. Furthermore, extensive experiments conducted on synthetic datasets
consistently validate these theoretical conclusions.Comment: 12 pages, 4 figure
OPTIMISATION OF HULL FORM OF OCEAN-GOING TRAWLER
This paper proposes a method for optimising the hull form of ocean-going trawlers to decrease resistance and consequently reduce the energy consumption. The entire optimisation process was managed by the integration of computer-aided design and computational fluid dynamics (CFD) in the CAESES software. Resistance was simulated using the CFD solver and STAR-CCM+. The ocean-going trawler was investigated under two main navigation conditions: trawling and design. Under the trawling condition, the main hull of the trawler was modified using the Lackenby method and optimised by NSGA-II algorithm and Sobol + Tsearch algorithm. Under the design condition, the bulbous bow was changed using the free-form deformation method, and the trawler was optimised by NSGA-Ⅱ. The best hull form is obtained by comparing the ship resistance under various design schemes. Towing experiments were conducted to measure the navigation resistance of trawlers before and after optimisation, thus verifying the reliability of the optimisation results. The results show that the proposed optimisation method can effectively reduce the resistance of trawlers under the two navigation conditions
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
Counterfactual inference for continuous rather than binary treatment
variables is more common in real-world causal inference tasks. While there are
already some sample reweighting methods based on Marginal Structural Model for
eliminating the confounding bias, they generally focus on removing the
treatment's linear dependence on confounders and rely on the accuracy of the
assumed parametric models, which are usually unverifiable. In this paper, we
propose a de-confounding representation learning (DRL) framework for
counterfactual outcome estimation of continuous treatment by generating the
representations of covariates disentangled with the treatment variables. The
DRL is a non-parametric model that eliminates both linear and nonlinear
dependence between treatment and covariates. Specifically, we train the
correlations between the de-confounded representations and the treatment
variables against the correlations between the covariate representations and
the treatment variables to eliminate confounding bias. Further, a
counterfactual inference network is embedded into the framework to make the
learned representations serve both de-confounding and trusted inference.
Extensive experiments on synthetic datasets show that the DRL model performs
superiorly in learning de-confounding representations and outperforms
state-of-the-art counterfactual inference models for continuous treatment
variables. In addition, we apply the DRL model to a real-world medical dataset
MIMIC and demonstrate a detailed causal relationship between red cell width
distribution and mortality.Comment: 15 pages,4 figure
VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference
Causal inference plays a vital role in diverse domains like epidemiology,
healthcare, and economics. De-confounding and counterfactual prediction in
observational data has emerged as a prominent concern in causal inference
research. While existing models tackle observed confounders, the presence of
unobserved confounders remains a significant challenge, distorting causal
inference and impacting counterfactual outcome accuracy. To address this, we
propose a novel variational learning model of unobserved confounders for
counterfactual inference (VLUCI), which generates the posterior distribution of
unobserved confounders. VLUCI relaxes the unconfoundedness assumption often
overlooked by most causal inference methods. By disentangling observed and
unobserved confounders, VLUCI constructs a doubly variational inference model
to approximate the distribution of unobserved confounders, which are used for
inferring more accurate counterfactual outcomes. Extensive experiments on
synthetic and semi-synthetic datasets demonstrate VLUCI's superior performance
in inferring unobserved confounders. It is compatible with state-of-the-art
counterfactual inference models, significantly improving inference accuracy at
both group and individual levels. Additionally, VLUCI provides confidence
intervals for counterfactual outcomes, aiding decision-making in risk-sensitive
domains. We further clarify the considerations when applying VLUCI to cases
where unobserved confounders don't strictly conform to our model assumptions
using the public IHDP dataset as an example, highlighting the practical
advantages of VLUCI.Comment: 15 pages, 8 figure
Genetic Removal of the CH1 Exon Enables the Production of Heavy Chain-Only IgG in Mice
Nano-antibodies possess great potential in many applications. However, they are naturally derived from heavy chain-only antibodies (HcAbs), which lack light chains and the CH1 domain, and are only found in camelids and sharks. In this study, we investigated whether the precise genetic removal of the CH1 exon of the γ1 gene enabled the production of a functional heavy chain-only IgG1 in mice. IgG1 heavy chain dimers lacking associated light chains were detected in the sera of the genetically modified mice. However, the genetic modification led to decreased expression of IgG1 but increased expression of other IgG subclasses. The genetically modified mice showed a weaker immune response to specific antigens compared with wild type mice. Using a phage-display approach, antigen-specific, single domain VH antibodies could be screened from the mice but exhibited much weaker antigen binding affinity than the conventional monoclonal antibodies. Although the strategy was only partially successful, this study confirms the feasibility of producing desirable nano-bodies with appropriate genetic modifications in mice
Comparison of Bypass Surgery with Drug-Eluting Stents in Diabetic Patients with Left Main Coronary Stenosis
∙ The authors have no financial conflicts of interest. Purpose: Several studies have compared the effects of coronary stenting and coronary-artery bypass grafting (CABG) on left main coronary artery (LMCA) disease. However, there are limited data on the long-term outcomes of these two interventions in diabetic patients. Materials and Methods: We evaluated 56 patients with LMCA stenosis who underwent drug-eluting stent (DES) implantation and 116 patients who underwent CABG in a single hospital in China between January 2004 and December 2006. We compared long-term major adverse cardiac events (death; a “serious outcome ” composite of death, myocardial infarction, or stroke; and targetvessel revascularization). Results: In-hospital (30-day) mortality was 0 % for the DES group and 3.4 % for the CABG group (p=0.31). There was no difference between the two groups in terms of risk of death [hazard ratio for stenting group, 0.49; 95 % confidence interval (CI), 0.13-1.63; p=0.55] or risk of serious outcome (hazar
Solar Ring Mission: Building a Panorama of the Sun and Inner-heliosphere
Solar Ring (SOR) is a proposed space science mission to monitor and study the
Sun and inner heliosphere from a full 360{\deg} perspective in the ecliptic
plane. It will deploy three 120{\deg}-separated spacecraft on the 1-AU orbit.
The first spacecraft, S1, locates 30{\deg} upstream of the Earth, the second,
S2, 90{\deg} downstream, and the third, S3, completes the configuration. This
design with necessary science instruments, e.g., the Doppler-velocity and
vector magnetic field imager, wide-angle coronagraph, and in-situ instruments,
will allow us to establish many unprecedented capabilities: (1) provide
simultaneous Doppler-velocity observations of the whole solar surface to
understand the deep interior, (2) provide vector magnetograms of the whole
photosphere - the inner boundary of the solar atmosphere and heliosphere, (3)
provide the information of the whole lifetime evolution of solar featured
structures, and (4) provide the whole view of solar transients and space
weather in the inner heliosphere. With these capabilities, Solar Ring mission
aims to address outstanding questions about the origin of solar cycle, the
origin of solar eruptions and the origin of extreme space weather events. The
successful accomplishment of the mission will construct a panorama of the Sun
and inner-heliosphere, and therefore advance our understanding of the star and
the space environment that holds our life.Comment: 41 pages, 6 figures, 1 table, to be published in Advances in Space
Researc
Parallel hierarchical cross entropy optimization for on-chip decap budgeting
Decoupling capacitor (decap) placement has been widely adopted as an effective way to suppress dynamic power supply noise. Traditional decap budgeting algorithms usually explore the sensitivity-based nonlinear optimizations or conjugate gradient methods, which can be prohibitively expensive for large-scale decap budgeting problems. We present a hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. CE is an advanced optimization framework which explores the power of rare-event probability theory and importance sampling. To achieve high efficiency, a sensitivity-guided cross entropy (SCE) algorithm is proposed which integrates CE with a partitioningbased sampling strategy to effectively reduce the dimensionality in solving the large scale decap budgeting problems. Extensive experiments on industrial power grid benchmarks show that the proposed SCE method converges 2X faster than the prior methods and 10X faster than the standard CE method, while gaining up to 25% improvement on power grid supply noise. Importantly, the proposed SCE algorithm is parallel-friendly since the simulation samples of each SCE iteration can be independently obtained in parallel. We obtain up to 1.9X speedup when running the SCE decap budgeting algorithm on a dual-core-dual-GPU system. Copyright 2010 ACM
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