80 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
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
Cardiac Hypertrophy Involves Both Myocyte Hypertrophy and Hyperplasia in Anemic Zebrafish
Background: An adult zebrafish heart possesses a high capacity of regeneration. However, it has been unclear whether and how myocyte hyperplasia contributes to cardiac remodeling in response to biomechanical stress and whether myocyte hypertrophy exists in the zebrafish. To address these questions, we characterized the zebrafish mutant tr265/tr265, whose Band 3 mutation disrupts erythrocyte formation and results in anemia. Although Band 3 does not express and function in the heart, the chronic anemia imposes a sequential biomechanical stress towards the heart. Methodology/principal findings: Hearts of the tr265/tr265 Danio rerio mutant become larger than those of the sibling by week 4 post fertilization and gradually exhibit characteristics of human cardiomyopathy, such as muscular disarray, re-activated fetal gene expression, and severe arrhythmia. At the cellular level, we found both increased individual cardiomyocyte size and increased myocyte proliferation can be detected in week 4 to week 12 tr265/tr265 fish. Interestingly, all tr265/tr265 fish that survive after week-12 have many more cardiomyocytes of smaller size than those in the sibling, suggesting that myocyte hyperplasia allows the long-term survival of these fish. We also show the cardiac hypertrophy process can be recapitulated in wild-type fish using the anemia-inducing drug phenylhydrazine (PHZ). Conclusions/significance: The anemia-induced cardiac hypertrophy models reported here are the first adult zebrafish cardiac hypertrophy models characterized. Unlike mammalian models, both cardiomyocyte hypertrophy and hyperplasia contribute to the cardiac remodeling process in these models, thus allowing the effects of cardiomyocyte hyperplasia on cardiac remodeling to be studied. However, since anemia can induce effects on the heart other than biomechanical, non-anemic zebrafish cardiac hypertrophy models shall be generated and characterized
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
Space advanced technology demonstration satellite
The Space Advanced Technology demonstration satellite (SATech-01), a mission for low-cost space science and new technology experiments, organized by Chinese Academy of Sciences (CAS), was successfully launched into a Sun-synchronous orbit at an altitude of similar to 500 km on July 27, 2022, from the Jiuquan Satellite Launch Centre. Serving as an experimental platform for space science exploration and the demonstration of advanced common technologies in orbit, SATech-01 is equipped with 16 experimental payloads, including the solar upper transition region imager (SUTRI), the lobster eye imager for astronomy (LEIA), the high energy burst searcher (HEBS), and a High Precision Magnetic Field Measurement System based on a CPT Magnetometer (CPT). It also incorporates an imager with freeform optics, an integrated thermal imaging sensor, and a multi-functional integrated imager, etc. This paper provides an overview of SATech-01, including a technical description of the satellite and its scientific payloads, along with their on-orbit performance
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