Non-causal Method Research Articles

Making predictions under interventions: a case study from the PREDICT-CVD cohort in New Zealand primary care.

Most existing clinical prediction models do not allow predictions under interventions. Such predictions allow predicted risk under different proposed strategies to be compared and are therefore useful to support clinical decision making. We aimed to compare methodological approaches for predicting individual level cardiovascular risk under three interventions: smoking cessation, reducing blood pressure, and reducing cholesterol. We used data from the PREDICT prospective cohort study in New Zealand to calculate cardiovascular risk in a primary care setting. We compared three strategies to estimate absolute risk under intervention: (a) conditioning on hypothetical interventions in non-causal models; (b) combining existing prediction models with causal effects estimated using observational causal inference methods; and (c) combining existing prediction models with causal effects reported in published literature. The median absolute cardiovascular risk among smokers was 3.9%; our approaches predicted that smoking cessation reduced this to a median between a non-causal estimate of 2.5% and a causal estimate of 2.8%, depending on estimation methods. For reducing blood pressure, the proposed approaches estimated a reduction of absolute risk from a median of 4.9% to a median between 3.2% and 4.5% (both derived from causal estimation). Reducing cholesterol was estimated to reduce median absolute risk from 3.1% to between 2.2% (non-causal estimate) and 2.8% (causal estimate). Estimated absolute risk reductions based on non-causal methods were different to those based on causal methods, and there was substantial variation in estimates within the causal methods. Researchers wishing to estimate risk under intervention should be explicit about their causal modelling assumptions and conduct sensitivity analysis by considering a range of possible approaches.

Analisis Kinerja Keuangan Perusahaan PT Indofood Cbp Sukses Makmur Tbk Sebelum dan Sesudah Akuisisi

This study was designed with the aim of digging up information on whether a company merger can be profitable and whether there are differences in the financial results of PT. Indofood Sukses Makmur uses liquidity (CR, QR), leverage (DER, DAR) and profitability (ROI, ROE). The method used in this study is a non-causal comparative method because it serves to provide an overview of the analysis of the relationship between status before and after the acquisition. The comparative non-causal method also provides information about the parameters and differences of several variables used in the research object through hypothesis testing. This research was conducted using quantitative descriptive analysis techniques and statistical tests (Paired Sample T-Test) to determine financial efficiency in companies PT. Indofood Sukses Makmur before and after the acquisition is based on a descriptive analysis method that varies up or down. The average ratio of changes experienced by the PT before the acquisition and after the acquisition. The company PT.Indofood Sukses Makmur's liquidity (CR and QR), profitability (ROI and ROE) and debt (DER and DAR) decreased.

Causal network inference and functional decomposition for decentralized statistical process monitoring: Detection and diagnosis

We propose a new systematic approach for conducting decentralized SPM based on the functional decomposition of the system’s causal network. The methodology consists of first inferring the causal network from normal operating data of the system under study, after which the functional modules are identified by exploring the graph topology and finding the strongly connected “communities”. The interaction between functional modules is also taken into account (macro-causality), by extending the original communities with the Markov-blankets of the connection nodes, giving rise to “extended communities”. Two hierarchical monitoring schemes are proposed for distributed monitoring: CNET-C (Causal Network-Centralized) and CNET-D (Causal Network-Distributed). Results demonstrate the increased sensitivity in fault detection of the proposed methodologies compared to conventional non-causal methods and centralized causal methods that monitor the complete network. The proposed approaches also lead to a more effective, unambiguous, and conclusive fault diagnosis activity.

Incorporating group update for speech enhancement based on convolutional gated recurrent network

To further improve the performance of speech enhancement methods based on deep neural networks, this paper proposes a speech enhancement network that can make full use of noisy speech characteristics in the time–frequency domain. First, based on the local correlation of noisy speech in the time–frequency domain and the spatial structure of frequency features, by using a recurrent neural network (RNN) to model the time correlation of noisy speech, and using a convolutional neural network (CNN) to calculate the frequency features of noisy speech, a convolutional gated recurrent network (CGRN) is built for speech enhancement. Second, based on the different variation characteristics of noisy speech over time, a group update mechanism is introduced to further improve CGRN; by artificially dividing the hidden layer features of the recurrent neural network in CGRN into three groups and updating them in three different ways, the CGRN incorporating group update (CGRN-GU) divides the variation characteristics roughly into three cases and can better track the changes of noisy speech over time. Finally, a causal speech enhancement method is proposed using the convolutional gated recurrent network incorporating group update, and extensive experiments are conducted on a public dataset. The experimental results show that, in the comparison with the state-of-the-art methods, the speech enhancement method proposed in this paper has better speech enhancement performance than other causal methods, and the proposed causal speech enhancement method even outperforms most non-causal methods.

A Unified View of Causal and Non-causal Feature Selection

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

Analytical Solution to Equivalent Consumption Minimization Strategy for Series Hybrid Electric Vehicles

Analytical optimal solution to the energy management of hybrid electric vehicles is of interest from theoretical and practical perspectives. Particularly, effort has been made to derive analytical solution to the energy management problem for series hybrid electric vehicles using Pontryagin's minimum principle (PMP). However, admissibility of the system input was not fully explored in determining the optimal input candidates. In this paper, the analytical solution for the same problem is found by partitioning the positive power demand set into four subsets, where the solution is derived for each case separately according to the corresponding admissible input set. The analytical solution is verified through comparison with numerical solution for a series hybrid electric wheel loader, and two different drive cycles are considered for this purpose. From the proposed solution, effective equivalence factor bounds are found and used to construct an adaptive equivalent consumption minimization strategy. The proposed strategy and the analytical solution are implemented together for the same vehicle to demonstrate their effectiveness in dealing with real-world applications. Simulations are performed for 12 drive cycles, and the results are compared to the ones achieved by PMP-based optimal control where the optimization is done numerically. Simulation results suggest that the proposed methodology is relatively fast and has satisfactory performance in presence of drive cycle uncertainty. It is observed that the proposed method fulfills charge sustenance, and the achieved fuel consumption figures are very close to the optimal benchmarks found by the non-causal method.

A Methodology for Shuttle Scheduling in Airports That Ensures Mitigating Arriving Passenger Congestion Under Uncertain Demand

Passenger congestion at the airport is a topic of significance in the field of air transportation in our day. In this study, a three-phased methodology, which warrants mitigating arriving passenger congestion at the airport by scheduling shuttle vehicles even under uncertain passenger demand, is proposed. In the first phase, monthly demands are forecast via an appropriate model from the family of noncausal methods. In the second phase, monthly forecasts are allocated throughout the whole day; then those forecasts are converted into uncertain parameters. In the last phase, a robust counterpart approach is employed to create a schedule for shuttle vehicles, which is absolutely immunized against uncertain demand. Istanbul Ataturk Airport is addressed for the case study. According to the simulation results, it is concluded that shuttle schedules, which are perfectly immunized against uncertainty in arriving passenger demand, are achieved by incurring reasonable increase in total operating cost.

Estimation of phase in EEG rhythms for real-time applications

Objective. Estimating the ongoing phase of oscillations in electroencephalography (EEG) recordings is an important aspect of understanding brain function, as well as for the development of phase-dependent closed-loop real-time systems that deliver stimuli. Such stimuli may take the form of direct brain stimulation (for example transcranial magnetic stimulation), or sensory stimuli (for example presentation of an auditory stimulus). We identify two linked problems related to estimating the phase of EEG rhythms with a specific focus on the alpha-band: 1) when the signal after a specific stimulus is unknown (real-time case), or 2) when it is corrupted by the presence of the stimulus itself (offline analysis). We propose methods to estimate the phase at the presentation time of these stimuli. Approach. Machine learning methods are used to learn the causal mapping from an unprocessed EEG recording to a phase estimate generated with a non-causal signal processing chain. This mapping is then used to predict the phase causally where non-causal methods are inappropriate. Main results. We demonstrate the ability of these machine learning methods to estimate instantaneous phase from an EEG signal subjected to very minor pre-processing with higher accuracy than commonly used signal-processing methods. Significance. Neural oscillations have been implicated in a wide variety of sensory, cognitive and motor functions. The instantaneous phase of these rhythms may reflect specific processes of computation which can be acted upon if they can be estimated with sufficient accuracy. Such brain-state dependent paradigms are of increasing medical and scientific interest.

Causal MSE-Optimal Filters for Personal Audio Subject to Constrained Contrast

A novel design method that generates causal pre-compensation filters is formulated. The resulting filters are constrained with respect to the amount of acoustic contrast they generate and are intended to be used for personal audio. The proposed method provides a more direct method for trading bright zone behavior against acoustic contrast as compared to other causal methods available. It also provides improved control over the temporal properties of the resulting filters as compared to the pre-existing non-causal methods. The resulting filters are analyzed by means of simulations, based on measured impulse responses of the sound-system–room interactions. The results of the simulations are compared to simulations of a frequency-domain optimal method with comparable objective, as proposed by Cai et al. and the results of the comparison are explained using the design equations. It is shown that the proposed method is viable, but that unattainable contrasts have a detrimental impact on the spectral bright zone behavior. A few different strategies for dealing with this problem are also proposed. It is demonstrated that the detrimental effect of increasingly strict causality constraints mainly concerns the lower frequency bright zone behavior in the system under investigation, but that the very highest attainable contrast levels may also be reduced somewhat.

Impact of Causality on Performance of Phasor Measurement Unit Algorithms

Most existing phasor measurement units (PMUs) are noncausal PMUs and they compensate for the group delay in PMU applications by shifting time stamps, which may result in high latency that will impair performance of PMU applications. This paper presents causal PMUs to reduce the group delay caused latency without shifting time stamps, and studies the impact of causality on performance of PMU algorithms. The paper first analyzes how causal and noncausal PMUs compensate for the group delay caused by digital filters and it is found that causal PMUs normally reduce latency by the group delay, whereas noncausal PMU will not. Then, the impact of causality on performance of PMU algorithms is investigated and analyzed by using the Hydro Quebec Research Institute (IREQ) PMU model and the IEEE PMU model. Finally, the simulation results carried out on two test systems highlight the difference between the causal method and the noncausal method under fault conditions in realistic multimachine setups.

Non-linear models for tourism demand forecasting

Quantitative methods for forecasting tourist arrivals can be subdivided into causal methods and non-causal methods. Non-causal time series methods remain popular tourism forecasting tools due to the accuracy of their forecasting ability and general ease of use. Since tourist arrivals exhibit seasonality, Seasonal Autoregressive Integrated Moving Average (SARIMA) models are often found to be the most accurate. However, these models assume that the time series is linear. This article compares the baseline seasonal Naïve and SARIMA forecasts of a seasonal tourist destination faced with a structural break in the data with alternative non-linear methods, with the aim of determining the accuracy of the various methods. These methods include the unobserved components model, smooth transition autoregressive model and singular spectrum analysis. The results show that the non-linear forecasts outperform the other methods. The linear methods show some superiority in short-term forecasts when there are no structural changes in the time series.

Causal estimation of neural and overall baroreflex sensitivity in relation to carotid artery stiffness

Continuous electrocardiogram, blood pressure and carotid artery ultrasound video were analyzed from 15 diabetics and 28 healthy controls. By using these measurements artery elasticity, overall baroreflex sensitivity (BRS) assessed between RR and systolic blood pressure variation, and neural BRS assessed between RR and artery diameter variation were estimated. In addition, BRS was estimated using traditional and causal methods which enable separation of feedforward and feedback variation. The aim of this study was to analyze overall and neural BRS in relation to artery stiffness and to validate the causal BRS estimation method in assessing these two types of BRS within the study population. The most significant difference between the healthy and diabetic groups (p < 0.0007) was found for the overall BRS estimated using the causal method. The difference between the groups was also significant for neural BRS (p < 0.0018). However neural BRS was normal in some old diabetics, which indicates normal functioning of autonomic nervous system (ANS), even though the elasticity in arteries of these subjects was reduced. The noncausal method overestimated neural BRS in low BRS values when compared to causal BRS. In conclusion, neural BRS estimated using the causal method is proposed as the best marker of ANS functioning.

Causal relationships between heart period and systolic arterial pressure during graded head-up tilt

In physiological conditions, heart period (HP) affects systolic arterial pressure (SAP) through diastolic runoff and Starling's law, but, the reverse relation also holds as a result of the continuous action of baroreflex control. The prevailing mechanism sets the dominant temporal direction in the HP-SAP interactions (i.e., causality). We exploited cross-conditional entropy to assess HP-SAP causality. A traditional approach based on phases was applied for comparison. The ability of the approach to detect the lack of causal link from SAP to HP was assessed on 8 short-term (STHT) and 11 long-term heart transplant (LTHT) recipients (i.e., less than and more than 2 yr after transplantation, respectively). In addition, spontaneous HP and SAP variabilities were extracted from 17 healthy humans (ages 21-36 yr, median age 29 yr; 9 females) at rest and during graded head-up tilt. The tilt table inclinations ranged from 15 to 75° and were changed in steps of 15°. All subjects underwent recordings at every step in random order. The approach detected the lack of causal relation from SAP to HP in STHT recipients and the gradual restoration of the causal link from SAP to HP with time after transplantation in the LTHT recipients. The head-up tilt protocol induced the progressive shift from the prevalent causal direction from HP to SAP to the reverse causality (i.e., from SAP to HP) with tilt table inclination in healthy subjects. Transformation of phases into time shifts and comparison with baroreflex latency supported this conclusion. The proposed approach is highly efficient because it does not require the knowledge of baroreflex latency. The dependence of causality on tilt table inclination suggests that "spontaneous" baroreflex sensitivity estimated using noncausal methods (e.g., spectral and cross-spectral approaches) is more reliable at the highest tilt table inclinations.

Forecasting British Tourist Arrivals in the Balearic Islands Using Meteorological Variables

This paper investigates the possibility of improving the predictive ability of a tourism demand model with meteorological explanatory variables. The authors use as a case study the monthly British tourism demand for the Balearic Islands (Spain). For this purpose, a transfer function model and causal artificial neural network are fitted. The results are compared with those obtained by non-causal methods: an ARIMA model and an autoregressive neural network. The results indicate that incorporating meteorological variables can increase predictive power, although the most accurate prediction is obtained using a non-causal model – specifically, an autoregressive neural network.

Photon propagation function and charged vector mesons

The one-loop contribution to the photon propagation function due to the exchange of a pair of vector mesons is presented in the context of source theory. An arbitrary magnetic moment κ is considered and the derived spectral form has the expected ultraviolet divergence (logarithmic when κ = 0, and linear when κ ≠ 0). This difficulty can be circumvented in the case κ = 0, when making use of a non-causal method based on the euclidean postulate, as stated in source theory, and Schwinger's modified propagation function for spin-1 particles, as derived by the author elsewhere.