We consider estimation of a total causal effect from observational data via covariate adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting knowledge of the underlying causal stru...
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We consider estimation of a total causal effect from observational data via covariate adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting knowledge of the underlying causal structure. Valid adjustment sets are, however, not unique. Recent research has introduced a graphical criterion for an 'optimal' valid adjustment set (O-set). For a given graph, adjustment by the O-set yields the smallest asymptotic variance compared to other adjustment sets in certain parametric and non-parametric models. In this paper, we provide three new results on the O-set. First, we give a novel, more intuitive graphical characterisation: We show that the O-set is the parent set of the outcome node(s) in a suitable latent projection graph, which we call the forbidden projection. An important property is that the forbidden projection preserves all information relevant to total causal effect estimation via covariate adjustment, making it a useful methodological tool in its own right. Second, we extend the existing ida algorithm to use the O-set, and argue that the algorithm remains semi-local. This is implemented in the R-package pcalg. Third, we present assumptions under which the O-set can be viewed as the target set of popular non-graphical variable selection algorithms such as stepwise backward selection.
We consider estimation of a total causal effect from observational data via covariate adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting knowledge of the underlying causal stru...
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We consider estimation of a total causal effect from observational data via covariate adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting knowledge of the underlying causal structure. Valid adjustment sets are, however, not unique. Recent research has introduced a graphical criterion for an 'optimal' valid adjustment set (O-set). For a given graph, adjustment by the O-set yields the smallest asymptotic variance compared to other adjustment sets in certain parametric and non-parametric models. In this paper, we provide three new results on the O-set. First, we give a novel, more intuitive graphical characterisation: We show that the O-set is the parent set of the outcome node(s) in a suitable latent projection graph, which we call the forbidden projection. An important property is that the forbidden projection preserves all information relevant to total causal effect estimation via covariate adjustment, making it a useful methodological tool in its own right. Second, we extend the existing ida algorithm to use the O-set, and argue that the algorithm remains semi-local. This is implemented in the R-package pcalg. Third, we present assumptions under which the O-set can be viewed as the target set of popular non-graphical variable selection algorithms such as stepwise backward selection.
As cloud storage has become more and more ubiquitous, there are a large number of consumers renting cloud storage services. However, as users lose direct control over the data, the integrity and availability of the ou...
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As cloud storage has become more and more ubiquitous, there are a large number of consumers renting cloud storage services. However, as users lose direct control over the data, the integrity and availability of the out sourced data become a big concern for users. Accordingly, how to verify the integrity of stored data and retrieve the availability of the corrupted data has become an urgent problem. Moreover, in most cases, users' data is not always static, but needs to be updated. In this paper, we propose a dynamic proof of retrievability scheme for cloud storage system, named as DIPOR. The DIPOR not only can retrieve the original data of corrupted blocks by using partial healthy data stored in healthy servers, but also support for updating operations of data. Furthermore, the number of forks M our scheme is not fixed, which means we can always look for the optimal forks based on the number of data blocks. In addition, the security analysis indicates that our scheme is provably secure and the performance evaluations show the efficiency of the proposed scheme.
With the arrival of big data era, cloud storage has become more ubiquitous. A growing number of consumers remote their data into cloud, as cloud can provide them with ample storage space and powerful computational cap...
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ISBN:
(纸本)9781467389990
With the arrival of big data era, cloud storage has become more ubiquitous. A growing number of consumers remote their data into cloud, as cloud can provide them with ample storage space and powerful computational capacity. However, storing data in cloud means that data is out of their control. How to verify the integrity of stored data and retrieve the corrupted data has become an urgent security problem. In this paper, we propose a new efficient proof of retrievability scheme, named as IPOR, for cloud storage systems. The IPOR not only can verify the integrity of remote data, but also can retrieve the original data of corrupted blocks from the healthy servers with probability 100%. Moreover, IPOR obviously decreases the complexity of data integrity tags and it requires performing a few multiplication and addition operations to retrieve the corrupted data. Therefore, our scheme is much more efficient than the state-of-the-art schemes. In addition, the security analysis indicates that our scheme is provably secure.
In this paper we describe a collection of heuristic search algorithms which use mixed ‘best-first’ and ‘depth-first’ strategies. These algorithms are designed to match the actual features of modern computers that ...
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