Causal Search in Structural Vector Autoregressive Models.

Journal of Machine Learning Research - Proceedings Track 01/2011; 12:95-114.
Source: DBLP
Download full-text


Available from: Nadine Chlaß, Jan 17, 2014
  • Source
    • "We assume that the underlying causal structure can be modeled as a directed graphical model G without simultaneous influence. There has been substantial work on modeling the statistics of time series, but relatively less on learning causal structure, and almost all of that assumes that the measurement and causal timescales match [1] [2] [3] [4] [5]. The problem of causal learning from " undersampled " time series data was explicitly addressed by [6] [7], but they assumed that the degree of undersampling—i.e., the ratio of τ S to τ M —was both known and small. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Causal structure learning from time series data is a major scientific challenge. Existing algorithms assume that measurements occur sufficiently quickly; more precisely, they assume that the system and measurement timescales are approximately equal. In many scientific domains, however, measurements occur at a significantly slower rate than the underlying system changes. Moreover, the size of the mismatch between timescales is often unknown. This paper provides three distinct causal structure learning algorithms, all of which discover all dynamic graphs that could explain the observed measurement data as arising from undersampling at some rate. That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus ``rate-agnostic.’’ We apply these algorithms to data from simulations. The results provide insight into the challenge of undersampling.
    Full-text · Conference Paper · Dec 2015
  • Source
    • "Accordingly, we need some extra information to provide Q. However, even if we introduce an orthonormality constraint QQ T = E t (U (t)U (t) T ) to make noises in W(t) mutually uncorrelated, the representation of the DVAR model given by this approach is not unique, because there are many choices for Q that satisfy that constraint, for example, QO satisfying QO(QO) T = QOO T Q T = QQ T , where O is any orthonormal matrix [Moneta et al. 2011]. There are many studies on this issue for identifying the SVAR model. "
    [Show abstract] [Hide abstract]
    ABSTRACT: A vector autoregressive model in discrete time domain (DVAR) is often used to analyze continuous time, multivariate, linear Markov systems through their observed time series data sampled at discrete timesteps. Based on previous studies, the DVAR model is supposed to be a noncanonical representation of the system, that is, it does not correspond to a unique system bijectively. However, in this article, we characterize the relations of the DVAR model with its corresponding Structural Vector AR (SVAR) and Continuous Time Vector AR (CTVAR) models through a finite difference method across continuous and discrete time domain. We further clarify that the DVARmodel of a continuous time,multivariate, linearMarkov system is canonical under a highly generic condition. Our analysis shows that we can uniquely reproduce its SVAR and CTVAR models from the DVAR model. Based on these results, we propose a novel Continuous and Structural Vector Autoregressive (CSVAR) modeling approach to derive the SVAR and the CTVAR models from their DVAR model empirically derived from the observed time series of continuous time linear Markov systems. We demonstrate its superior performance through some numerical experiments on both artificial and real-world data.
    Preview · Article · Nov 2015 · ACM Transactions on Intelligent Systems and Technology
  • Source
    • "Besides, the variables imports, dependency ratio and population density were dropped after tests of stationary proved them to be of different order of integration. Moreover, the statistical performance of the estimates from VAR and VEC models has been well studied and well established for models with a few number of variables (Moneta et al., 2011). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Ethiopia is one of the countries with high fertility, rapidly growing and largely young population. At the same time, it is among countries with weak and poorly focused population policy. In light of this, this study intended to assess the causation between demographic factors and economic development in Ethiopia. To this end, it applied vector-error-correction model (VECM) to data on economic, demographic and other variables obtained from secondary sources, accompanied by descriptive analysis of the relationship of population with HDI, agricultural landholdings and forestland. VECM results indicated robust and negative long run relationship between per capita income and population growth and a positive one between the former and growth of workers – with bidirectional causality in both cases. That is, rises in per capita income reduce the growth of (dependent) population and enhance that of workers, and vice versa. Conversely, slower growth of population or faster growth of workers raises per capita income. Short run relationships turned out to be weak and non-robust to alternative model specifications. The descriptive analysis signified inverse associations of population growth with landholding, forest coverage and HDI score. These findings point to a need for meaningful efforts to incorporate population matters into the policy arena.
    Full-text · Article · Oct 2012
Show more