Recently, several approaches have been proposed to discover the causality of the time-independent or fixed causal model. However, in many realistic applications, especially in economics and neuroscience, causality amo...Recently, several approaches have been proposed to discover the causality of the time-independent or fixed causal model. However, in many realistic applications, especially in economics and neuroscience, causality among variables might be time-varying. A time-varying linear causal model with non-Gaussian noise is considered and the estimation of the causal model from observational data is focused. Firstly, an independent component analysis(ICA) based two stage method is proposed to estimate the time-varying causal coefficients. It shows that, under appropriate assumptions, the time varying coefficients in the proposed model can be estimated by the proposed approach, and results of experiment on artificial data show the effectiveness of the proposed approach. And then, the granger causality test is used to ascertain the causal direction among the variables. Finally, the new approach is applied to the real stock data to identify the causality among three stock indices and the result is consistent with common sense.展开更多
The main purpose in many randomized trials is to make an inference about the average causal effect of a treatment. Therefore, on a binary outcome, the null hypothesis for the hypothesis test should be that the causal ...The main purpose in many randomized trials is to make an inference about the average causal effect of a treatment. Therefore, on a binary outcome, the null hypothesis for the hypothesis test should be that the causal risks are equal in the two groups. This null hypothesis is referred to as the weak causal null hypothesis. Nevertheless, at present, hypothesis tests applied in actual randomized trials are not for this null hypothesis;Fisher’s exact test is a test for the sharp causal null hypothesis that the causal effect of treatment is the same for all subjects. In general, the rejection of the sharp causal null hypothesis does not mean that the weak causal null hypothesis is rejected. Recently, Chiba developed new exact tests for the weak causal null hypothesis: a conditional exact test, which requires that a marginal total is fixed, and an unconditional exact test, which does not require that a marginal total is fixed and depends rather on the ratio of random assignment. To apply these exact tests in actual randomized trials, it is inevitable that the sample size calculation must be performed during the study design. In this paper, we present a sample size calculation procedure for these exact tests. Given the sample size, the procedure can derive the exact test power, because it examines all the patterns that can be obtained as observed data under the alternative hypothesis without large sample theories and any assumptions.展开更多
时序数据存在近因性特点,即变量值普遍依赖近期的历史信息,而现有时序因果推断方法没有充分考虑时序数据的这种特性,在通过假设检验推断不同延迟的因果关系时使用统一的阈值,难以有效推断较弱的因果关系。针对上述问题,提出基于自适应...时序数据存在近因性特点,即变量值普遍依赖近期的历史信息,而现有时序因果推断方法没有充分考虑时序数据的这种特性,在通过假设检验推断不同延迟的因果关系时使用统一的阈值,难以有效推断较弱的因果关系。针对上述问题,提出基于自适应阈值学习的时序因果推断方法:首先提取数据特性,其次根据不同延迟下数据呈现的性质,自动地学习假设检验过程中使用的阈值组合,最后将该阈值组合用于PC(Peter-Clark)算法、PCMCI(Peter-Clark and Momentary Conditional Independence)算法和VAR-LINGAM(Vector AutoRegressive LINear non-Gaussian Acyclic Model)算法的假设检验过程,以得到更准确的因果关系结构。在仿真数据集上的实验结果表明,采用所提方法的自适应PC算法、自适应PCMCI算法和自适应VAR-LINGAM算法的F1值都有所提高。展开更多
Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict ...Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict future dynamics. Due to the advances of modern technology, big data becomes increasingly accessible and consequently the problem of reconstructing systems from measured data or time series plays a central role in many scientific disciplines. In recent decades, nonlinear methods rooted in state space reconstruction have been developed, and they do not assume any model equations but can recover the dynamics purely from the measured time series data. In this review, the development of state space reconstruction techniques will be introduced and the recent advances in systems prediction and causality inference using state space reconstruction will be presented. Particularly, the cutting-edge method to deal with short-term time series data will be focused on.Finally, the advantages as well as the remaining problems in this field are discussed.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.61573014)
文摘Recently, several approaches have been proposed to discover the causality of the time-independent or fixed causal model. However, in many realistic applications, especially in economics and neuroscience, causality among variables might be time-varying. A time-varying linear causal model with non-Gaussian noise is considered and the estimation of the causal model from observational data is focused. Firstly, an independent component analysis(ICA) based two stage method is proposed to estimate the time-varying causal coefficients. It shows that, under appropriate assumptions, the time varying coefficients in the proposed model can be estimated by the proposed approach, and results of experiment on artificial data show the effectiveness of the proposed approach. And then, the granger causality test is used to ascertain the causal direction among the variables. Finally, the new approach is applied to the real stock data to identify the causality among three stock indices and the result is consistent with common sense.
文摘The main purpose in many randomized trials is to make an inference about the average causal effect of a treatment. Therefore, on a binary outcome, the null hypothesis for the hypothesis test should be that the causal risks are equal in the two groups. This null hypothesis is referred to as the weak causal null hypothesis. Nevertheless, at present, hypothesis tests applied in actual randomized trials are not for this null hypothesis;Fisher’s exact test is a test for the sharp causal null hypothesis that the causal effect of treatment is the same for all subjects. In general, the rejection of the sharp causal null hypothesis does not mean that the weak causal null hypothesis is rejected. Recently, Chiba developed new exact tests for the weak causal null hypothesis: a conditional exact test, which requires that a marginal total is fixed, and an unconditional exact test, which does not require that a marginal total is fixed and depends rather on the ratio of random assignment. To apply these exact tests in actual randomized trials, it is inevitable that the sample size calculation must be performed during the study design. In this paper, we present a sample size calculation procedure for these exact tests. Given the sample size, the procedure can derive the exact test power, because it examines all the patterns that can be obtained as observed data under the alternative hypothesis without large sample theories and any assumptions.
文摘时序数据存在近因性特点,即变量值普遍依赖近期的历史信息,而现有时序因果推断方法没有充分考虑时序数据的这种特性,在通过假设检验推断不同延迟的因果关系时使用统一的阈值,难以有效推断较弱的因果关系。针对上述问题,提出基于自适应阈值学习的时序因果推断方法:首先提取数据特性,其次根据不同延迟下数据呈现的性质,自动地学习假设检验过程中使用的阈值组合,最后将该阈值组合用于PC(Peter-Clark)算法、PCMCI(Peter-Clark and Momentary Conditional Independence)算法和VAR-LINGAM(Vector AutoRegressive LINear non-Gaussian Acyclic Model)算法的假设检验过程,以得到更准确的因果关系结构。在仿真数据集上的实验结果表明,采用所提方法的自适应PC算法、自适应PCMCI算法和自适应VAR-LINGAM算法的F1值都有所提高。
基金supported by the National Key Research and Development Program of China (Grant No. 2017YFA0505500)Japan Society for the Promotion of Science KAKENHI Program (Grant No. JP15H05707)National Natural Science Foundation of China (Grant Nos. 11771010,31771476,91530320, 91529303,91439103 and 81471047)
文摘Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict future dynamics. Due to the advances of modern technology, big data becomes increasingly accessible and consequently the problem of reconstructing systems from measured data or time series plays a central role in many scientific disciplines. In recent decades, nonlinear methods rooted in state space reconstruction have been developed, and they do not assume any model equations but can recover the dynamics purely from the measured time series data. In this review, the development of state space reconstruction techniques will be introduced and the recent advances in systems prediction and causality inference using state space reconstruction will be presented. Particularly, the cutting-edge method to deal with short-term time series data will be focused on.Finally, the advantages as well as the remaining problems in this field are discussed.
