Causal Search in Structural Vector Autoregressive Models.

Journal of Machine Learning Research - Proceedings Track 01/2011; 12:95-114.
Source: DBLP
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    ABSTRACT: A very important topic in systems biology is developing statistical methods that automatically find causal relations in gene regulatory networks with no prior knowledge of causal connectivity. Many methods have been developed for time series data. However, discovery methods based on steady-state data are often necessary and preferable since obtaining time series data can be more expensive and/or infeasible for many biological systems. A conventional approach is causal Bayesian networks. However, estimation of Bayesian networks is ill-posed. In many cases it cannot uniquely identify the underlying causal network and only gives a large class of equivalent causal networks that cannot be distinguished between based on the data distribution. We propose a new discovery algorithm for uniquely identifying the underlying causal network of genes. To the best of our knowledge, the proposed method is the first algorithm for learning gene networks based on a fully identifiable causal model called LiNGAM. We here compare our algorithm with competing algorithms using artificially-generated data, although it is definitely better to test it based on real microarray gene expression data.
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    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.
    10/2012; 4(10):236-251. DOI:10.5897/JEIF12.039

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