393156
A general identification condition for causal effects
This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive assumptions. The assumptions are encoded in the form of a directed acyclic graph, also called "causal graph", in which some variables are presumed to be unobserved. The paper establishes a necessary and sufficient criterion for the identifiability of the causal effects of a singleton variable on all other variables in the model, and a powerful sufficient criterion for the effects of a singleton variable on any set of variables.
15872261
Identifying Direct Causal Effects in Linear Models
This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. Using techniques developed for graphical causal models, we show that a model can be decomposed into a set of submodels such that the identification problem can be solved independently in each submodel. We provide a new identification method that identifies causal effects by solving a set of algebraic equations.
3092732
Inequality Constraints in Causal Models with Hidden Variables
We present a class of inequality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network, in which some of the variables remain unmeasured. We derive bounds on causal effects that are not directly measured in randomized experiments. We derive instrumental inequality type of constraints on nonexperimental distributions. The results have applications in testing causal models with observational or experimental data.
10434536
Identifying Linear Causal Effects
This paper concerns the assessment of linear cause-effect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and thus provides a new approach for assessing linear causal effects in structural equation models. Using this approach the paper develops a systematic procedure for recognizing identifiable direct causal effects.
4621823
On the Testable Implications of Causal Models with Hidden Variables
The validity of a causal model can be tested only if the model imposes constraints on the probability distribution that governs the generated data. In the presence of unmeasured variables, causal models may impose two types of constraints: conditional independencies, as read through the d-separation criterion, and functional constraints, for which no general criterion is available. This paper offers a systematic way of identifying functional constraints and, thus, facilitates the task of testing causal models as well as inferring such models from data.
31406629
Fingerprint Matching with Registration Pattern Inspection
The "registration pattern" between two fingerprints is the optimal registration of each part of one fingerprint with respect to the other fingerprint. Registration patterns generated from imposter's matching attempts are different from those patterns from genuine matching attempts, although they may share some similarities in the aspect of minutiae. In this paper, we present an algorithm that utilizes minutiae, associate ridges and orientation fields to determine the registration pattern between two fingerprints and match them. The proposed matching scheme has two stages. An offline, training stage, derives a genuine registration pattern base from a set of genuine matching attempts. Then, an online matching stage registers the two fingerprints and determines the registration pattern. Only if the pattern makes a genuine one, a further fine matching is conducted. The genuine registration pattern base was derived using a set of fingerprints extracted from the NIST Special Database 24. The algorithm has been tested on the second FVC2002 database. Experimental results demonstrate the performance of the proposed algorithm.
19924236
Test-Execution-Based Reliability Measurement and Modeling for Large Commercial Software
The paper studies practical reliability measurement and modeling for large commercial software systems based on test execution data collected during system testing. The application environment and the goals of reliability assessment were analyzed to identify appropriate measurement data. Various reliability growth models were used on failure data normalized by test case executions to track testing progress and provide reliability assessment. Practical problems in data collection, reliability measurement and modeling, and modeling result analysis were also examined. The results demonstrated the feasibility of reliability measurement in a large commercial software development environment and provided a practical comparison of various reliability measurements and models under such an environment. >
18528442
Better Reliability Assessment and Prediction through Data Clustering
This paper presents a new approach to software reliability modeling by grouping data into clusters of homogeneous failure intensities. This series of data clusters associated with different time segments can be directly used as a piecewise linear model for reliability assessment and problem identification, which can produce meaningful results early in the testing process. The dual model fits traditional software reliability growth models (SRGMs) to these grouped data to provide long-term reliability assessments and predictions. These models were evaluated in the testing of two large software systems from IBM. Compared with existing SRGMs fitted to raw data, our models are generally more stable over time and produce more consistent and accurate reliability assessments and predictions.
25090129
Comments on "A translation- and scale-invariant adaptive wavelet transform"
Xiong et al. presented a translationand scale-invariant adaptive wavelet transform. We show that their renormalized signal (pre-wavelet transform) is already translation- and scale-invariant. Thus, it is not just invariant under the wavelet transform. We give a necessary and sufficient condition for renormalized signals to be affine transform invariant.
7621899
Test workload measurement and reliability analysis for large commercial software systems
This paper characterizes the testing environment for large commercial software systems, matches reliability model assumptions with the application environment, examines alternative test workload measurements that capture software usage information during testing, and uses two such measurements, test runs and transactions, as our usage dependent time measurements in reliability modeling. Our previous research using test runs, execution time, and test input information for reliability analysis and improvement is extended to ensure better test workload measurements for reliability assessment and prediction. This paper also identifies conditions under which different test workload measurements are appropriate, and presents reliability modeling results using these measurements in several products developed in the IBM Software Solutions Toronto Laboratory.