Making causal inferences from observational studies can be challenging when confounders are missing not at random. In such cases, identifying causal effects is often not guaranteed. Motivated by a real example, we consider a treatment-independent missingness assumption under which we establish the identification of causal effects when confounders are missing not at random. We propose a weighted estimating equation (WEE) approach for estimating model parameters and introduce three estimators for the average causal effect, based on regression, propensity score weighting, and doubly robust estimation. We evaluate the performance of these estimators through simulations, and provide a real data analysis to illustrate our proposed method.
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Operations research deals with modeling and solving real-world problems as mathematical optimization problems. While solving mathematical systems is accomplished by analytical software, formulating a problem as a set of mathematical operations has been typically done manually by domain experts. Recent machine learning methods have shown promise in converting textual problem descriptions to corresponding mathematical formulations. This paper presents an approach that converts linear programming word problems into mathematical formulations. We leverage the named entities in the input and augment the input to highlight these entities. Our approach achieves the highest accuracy among all submissions to the NL4Opt Competition, securing first place in the generation track.
Morphing quadrotors with four external actuators can adapt to different restricted scenarios by changing their geometric structure. However, previous works mainly focus on the improvements in structures and controllers, and existing planning algorithms don't consider the morphological modifications, which leads to safety and dynamic feasibility issues. In this paper, we propose a unified planning and control framework for morphing quadrotors to deform autonomously and efficiently. The framework consists of a milliseconds-level spatial-temporal trajectory optimizer that takes into account the morphological modifications of quadrotors. The optimizer can generate full-body safety trajectories including position and attitude. Additionally, it incorporates a nonlinear attitude controller that accounts for aerodynamic drag and dynamically adjusts dynamic parameters such as the inertia tensor and Center of Gravity. The controller can also online compute the thrust coefficient during morphing. Benchmark experiments compared with existing methods validate the robustness of the proposed controller. Extensive simulations and real-world experiments are performed to demonstrate the effectiveness of the proposed framework.
One of the fundamental challenges in drawing causal inferences from observational studies is that the assumption of no unmeasured confounding is not testable from observed data. Therefore, assessing sensitivity to this assumption's violation is important to obtain valid causal conclusions in observational studies. Although several sensitivity analysis frameworks are available in the casual inference literature, very few of them are applicable to observational studies with multivalued treatments. To address this issue, we propose a sensitivity analysis framework for performing sensitivity analysis in multivalued treatment settings. Within this framework, a general class of additive causal estimands has been proposed. We demonstrate that the estimation of the causal estimands under the proposed sensitivity model can be performed very efficiently. Simulation results show that the proposed framework performs well in terms of bias of the point estimates and coverage of the confidence intervals when there is sufficient overlap in the covariate distributions. We illustrate the application of our proposed method by conducting an observational study that estimates the causal effect of fish consumption on blood mercury levels.
When interest lies in the progression of a disease rather than on a single outcome, non-homogeneous multi-state Markov models constitute a natural and powerful modelling approach. Constant monitoring of a phenomenon of interest is often unfeasible, hence leading to an intermittent observation scheme. This setting is challenging and existing models and their implementations do not yet allow for flexible enough specifications that can fully exploit the information contained in the data. To widen significantly the scope of multi-state Markov models, we propose a closed-form expression for the local curvature information of a key quantity, the transition probability matrix. Such development allows one to model any type of multi-state Markov process, where the transition intensities are flexibly specified as functions of additive predictors. Parameter estimation is carried out through a carefully structured, stable penalised likelihood approach. The methodology is exemplified via two case studies that aim at modelling the onset of cardiac allograft vasculopathy, and cognitive decline. To support applicability and reproducibility, all developed tools are implemented in the R package flexmsm.
The growth and progression of brain tumors is governed by patient-specific dynamics. Even when the tumor appears well-delineated in medical imaging scans, tumor cells typically already have infiltrated the surrounding brain tissue beyond the visible lesion boundaries. Quantifying and understanding these growth dynamics promises to reveal this otherwise hidden spread and is key to individualized therapies. Current treatment plans for brain tumors, such as radiotherapy, typically involve delineating a standard uniform margin around the visible tumor on imaging scans to target this invisible tumor growth. This "one size fits all" approach is derived from population studies and often fails to account for the nuances of individual patient conditions. Here, we present the framework GliODIL which infers the full spatial distribution of tumor cell concentration from available imaging data based on PDE-constrained optimization. The framework builds on the newly introduced method of Optimizing the Discrete Loss (ODIL), data are assimilated in the solution of the Partial Differential Equations (PDEs) by optimizing a cost function that combines the discrete form of the equations and data as penalty terms. By utilizing consistent and stable discrete approximations of the PDEs, employing a multigrid method, and leveraging automatic differentiation, we achieve computation times suitable for clinical application such as radiotherapy planning. Our method performs parameter estimation in a manner that is consistent with the PDEs. Through a harmonious blend of physics-based constraints and data-driven approaches, GliODIL improves the accuracy of estimating tumor cell distribution and, clinically highly relevant, also predicting tumor recurrences, outperforming all other studied benchmarks.
Accountability in the workplace is critically important and remains a challenging problem, especially with respect to workplace safety management. In this paper, we introduce a novel notion, the Internet of Responsibilities, for accountability management. Our method sorts through the list of responsibilities with respect to hazardous positions. The positions are interconnected using directed acyclic graphs (DAGs) indicating the hierarchy of responsibilities in the organization. In addition, the system detects and collects responsibilities, and represents risk areas in terms of the positions of the responsibility nodes. Finally, an automatic reminder and assignment system is used to enforce a strict responsibility control without human intervention. Using blockchain technology, we further extend our system with the capability to store, recover and encrypt responsibility data. We show that through the application of the Internet of Responsibility network model driven by Big Data, enterprise and government agencies can attain a highly secured and safe workplace. Therefore, our model offers a combination of interconnected responsibilities, accountability, monitoring, and safety which is crucial for the protection of employees and the success of organizations.
The fusion of causal models with deep learning introducing increasingly intricate data sets, such as the causal associations within images or between textual components, has surfaced as a focal research area. Nonetheless, the broadening of original causal concepts and theories to such complex, non-statistical data has been met with serious challenges. In response, our study proposes redefinitions of causal data into three distinct categories from the standpoint of causal structure and representation: definite data, semi-definite data, and indefinite data. Definite data chiefly pertains to statistical data used in conventional causal scenarios, while semi-definite data refers to a spectrum of data formats germane to deep learning, including time-series, images, text, and others. Indefinite data is an emergent research sphere inferred from the progression of data forms by us. To comprehensively present these three data paradigms, we elaborate on their formal definitions, differences manifested in datasets, resolution pathways, and development of research. We summarize key tasks and achievements pertaining to definite and semi-definite data from myriad research undertakings, present a roadmap for indefinite data, beginning with its current research conundrums. Lastly, we classify and scrutinize the key datasets presently utilized within these three paradigms.
Understanding causality helps to structure interventions to achieve specific goals and enables predictions under interventions. With the growing importance of learning causal relationships, causal discovery tasks have transitioned from using traditional methods to infer potential causal structures from observational data to the field of pattern recognition involved in deep learning. The rapid accumulation of massive data promotes the emergence of causal search methods with brilliant scalability. Existing summaries of causal discovery methods mainly focus on traditional methods based on constraints, scores and FCMs, there is a lack of perfect sorting and elaboration for deep learning-based methods, also lacking some considers and exploration of causal discovery methods from the perspective of variable paradigms. Therefore, we divide the possible causal discovery tasks into three types according to the variable paradigm and give the definitions of the three tasks respectively, define and instantiate the relevant datasets for each task and the final causal model constructed at the same time, then reviews the main existing causal discovery methods for different tasks. Finally, we propose some roadmaps from different perspectives for the current research gaps in the field of causal discovery and point out future research directions.
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SciIE) for with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.
We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact. Adopting a straightforward decomposition of the problem into entity detection, entity linking, relation prediction, and evidence combination, we explore simple yet strong baselines. On the popular SimpleQuestions dataset, we find that basic LSTMs and GRUs plus a few heuristics yield accuracies that approach the state of the art, and techniques that do not use neural networks also perform reasonably well. These results show that gains from sophisticated deep learning techniques proposed in the literature are quite modest and that some previous models exhibit unnecessary complexity.
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