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What makes a readable code? A causal analysis method

SOFTWARE-PRACTICE & EXPERIENCE 2023年第6期53卷 1391-1409页

作者： Mi, Qing Chen, Mingjie Cai, Zhi Jia, Xibin Beijing Univ Technol Fac Informat Technol Beijing Peoples R China South China Univ Technol Sch Software Engn Guangzhou Peoples R China Beijing Univ Technol Fac Informat Technol 100 PingleyuanChaoyang Dist Beijing Peoples R China

Context:Code readability is one of the most important quality attributes for software source code. To investigate which features affect code readability, most existing studies rely on correlation-based methods. However, spurious correlations (a mathematical relationship wherein two variables appear to be causal but are not) involved in correlation-based methods may affect research conclusions. Objective:In order to remove spurious correlations and obtain conclusions from the perspective of causation as to what makes a readable code, we propose a causal theory-based approach to analyze the relationship between code features and code readability scores. Method:First, we adopt the pc algorithm and additive noise models to construct the causal graph on the basis of the selected code features. Then, we use the linear regression algorithm based on the back-door criterion to obtain the causal effect of different features on code readability. Result:We conduct a set of experiments on readability data labeled by human annotators. The experimental results show that the average number of comments positively impacts code readability, with each additional unit increasing the code readability score by 0.799 points. Whereas the average number of assignments, identifiers, and periods have a negative impact, with each additional unit decreasing the code readability score by 0.528, 0.281, and 0.170 points respectively. Conclusion:We believe that our findings will provide developers with a better understanding of the patterns behind code readability, and guide developers to optimize their code as the ultimate goal.

关键词： causal analysis code readability pc algorithm program comprehension

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Contemporaneous causality among office property prices of major Chinese cities with vector error correction modeling and directed acyclic graphs

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JOURNAL OF MODELLING IN MANAGEMENT 2024年第4期19卷 1079-1093页

作者： Xu, Xiaojie Zhang, Yun North Carolina State Univ Raleigh Raleigh NC 27695 USA

PurposeThis study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years ***/methodology/approachUsing monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 *** pc algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of ***/valueThis suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities.

关键词： Artificial intelligence Modelling Policy algorithms Econometrics Office property price index Vector error correction modeling Directed acyclic graph pc algorithm Linear non-Gaussian acyclic model

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Contemporaneous causality among regional steel price indices of east, south, north, central south, northeast, southwest, and northwest China

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MINERAL ECONOMICS 2024年第1期37卷 1-14页

作者： Xu, Xiaojie Zhang, Yun North Carolina State Univ Raleigh NC 27695 USA

In this study, we aim at investigating dynamic relations among steel price indices of all seven regional markets in China, spanning the period of 2010M1-2021M4. With daily data, we apply techniques of vector error correction modeling and the directed acyclic graph to analyze the issue of contemporaneous causality among the seven indices, the latter of which is facilitated through the LiNGAM algorithm. We find via vector error correction modeling that each of the seven indices is part of cointegration relations and responses to long-run equilibrium disturbances. We arrive at causal paths via the LiNGAM algorithm for enabling innovation accounting analysis. With this analysis, we find sophisticated dynamics in price adjustment processes following shocks, for which east and northeast regions have the most significant impacts on other regions, followed by south and north regions, followed by the southwest region. Central south and northwest regions are not found to have significant impacts on others. These results suggest that policies on regional steel prices in the long run might be designed with most attention paid to five of the seven regions, particularly east and northeast regions.

关键词： Steel price index Vector error correction model Directed acyclic graph pc algorithm Linear non-Gaussian acyclic model

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Energy intensity determinants based on structure-oriented cointegration by embedding a knowledge box in a time series model: evidence from Iran

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第9期29卷 13504-13522页

作者： Esmaeili, Parisa Rafei, Meysam Kharazmi Univ Fac Econ Tehran Iran

Energy intensity reduction is an exigent issue for Iran, where energy consumption is so high. Therefore, finding effective policies to reduce energy intensity is essential. With this in mind, the impact of financial development, government investment, oil revenues, and trade openness on energy intensity is assessed in this study. We combined structural vector error correction model (SVECM) and directed acyclic graphs (DAG) technique to examine the relationships between study variables. The results of DAG prove that financial development, government investment, oil revenues, and trade openness influence the intensity of energy. Besides, the significant and long-run relationships among variables allowed us to apply SVECM. Impulse response functions and variance decomposition analysis indicate that government investment, oil revenues, and trade openness are negatively associated with the intensity of energy. Also, financial development positively influences energy intensity. Meanwhile, the impact of government investment is more significant than oil revenues, trade openness, and financial development impacts. So, government investment is the most effective policy regarding optimizing the consumption of energy and reducing energy intensity. We also advise policymakers to use oil revenues to increase government investment, enhance trade openness, and tax the private sector to improve the level of energy intensity.

关键词： Energy intensity SVECM DAG pc algorithm Cointegration Energy policy

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Partitioned hybrid learning of Bayesian network structures

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MACHINE LEARNING 2022年第5期111卷 1695-1738页

作者： Huang, Jireh Zhou, Qing Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA

We develop a novel hybrid method for Bayesian network structure learning called partitioned hybrid greedy search (pHGS), composed of three distinct yet compatible new algorithms: Partitioned pc (ppc) accelerates skeleton learning via a divide-and-conquer strategy, p-value adjacency thresholding (PATH) effectively accomplishes parameter tuning with a single execution, and hybrid greedy initialization (HGI) maximally utilizes constraint-based information to obtain a high-scoring and well-performing initial graph for greedy search. We establish structure learning consistency of our algorithms in the large-sample limit, and empirically validate our methods individually and collectively through extensive numerical comparisons. The combined merits of ppc and PATH achieve significant computational reductions compared to the pc algorithm without sacrificing the accuracy of estimated structures, and our generally applicable HGI strategy reliably improves the estimation structural accuracy of popular hybrid algorithms with negligible additional computational expense. Our empirical results demonstrate the competitive empirical performance of pHGS against many state-of-the-art structure learning algorithms.

关键词： Bayesian networks Directed acyclic graphs Structure learning Greedy search pc algorithm Divide-and-conquer

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Fiscal sustainability, monetary policy and economic growth in the Euro Area: In search of the ultimate causal path

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ECONOMIC ANALYSIS AND POLICY 2023年 78卷 1026-1045页

作者： Golpe, Antonio A. Sanchez-Fuentes, A. Jesus Vides, Jose Carlos Univ Huelva Huelva Spain ICEI Complutense Univ Madrid Madrid Spain GEN UVigo Vigo Spain

To assess the ultimate causal flow between monetary policy indicators, fiscal sustainability and economic growth has been deeply studied in the literature. However, this issue is still open to discussion due to mixed results and caveats/limitations of existing studies. Importantly, previous analyses mostly focus on bivariate/trivariate systems, missing a relevant piece of economic drivers. We analyse jointly these interdependencies by applying multivariate Granger Causality and determining an ultimate "causality path" excluding redundant relationships. Thus, we combine recent developments introduced to estimate the Granger causality procedure based on Meta-analysis in heterogeneous mixed panels and graphical models searching iteratively for the existing dependencies between a multivariate set of information. Our results provide novel empirical evidence suggesting that monetary policy variables play a leading role in the resulting complex economic system. Furthermore, we do find evidence supporting the role of Total Expenditure as a driver of fiscal policy. (c) 2023 Economic Society of Australia, Queensland. Published by Elsevier B.V.

关键词： Granger causality Monetary policy Public finance Causal maps pc algorithm

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Contemporaneous causality among residential housing prices of ten major Chinese cities

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INTERNATIONAL JOURNAL OF HOUSING MARKETS AND ANALYSIS 2023年第4期16卷 792-811页

作者： Xu, Xiaojie Zhang, Yun North Carolina State Univ Raleigh NC 27695 USA

Purpose This study aims to investigate dynamic relationships among residential housing price indices of ten major Chinese cities for the years 2005-2021. Design/methodology/approach Using monthly data, this study uses vector error correction modeling and the directed acyclic graph for characterization of contemporaneous causality among the ten indices. Findings The pc algorithm identifies the causal pattern and the Linear Non-Gaussian Acyclic Model algorithm further determines the causal path, from which this study conducts innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tiers of cities. Originality/value This study suggests that policies on residential housing prices in the long run might need to be planned with particular attention paid to these top tiers of cities.

关键词： Residential housing price index Vector error correction model Directed acyclic graph pc algorithm Linear non-Gaussian acyclic model Chinese market

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Causal discoveries for high dimensional mixed data

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STATISTICS IN MEDICINE 2022年第24期41卷 4924-4940页

作者： Cai, Zhanrui Xi, Dong Zhu, Xuan Li, Runze Iowa State Univ Dept Stat Ames IA USA Gilead Sci Foster City CA 94404 USA Novartis Pharmaceut E Hanover NJ USA Penn State Univ Dept Stat University Pk PA 16802 USA

Causal relationships are of crucial importance for biological and medical research. algorithms have been proposed for causal structure learning with graphical visualizations. While much of the literature focuses on biological studies where data often follow the same distribution, for example, the normal distribution for all variables, challenges emerge from epidemiological and clinical studies where data are often mixed with continuous, binary, and ordinal variables. We propose to use a mixed latent Gaussian copula model to estimate the underlying correlation structure via the rank correlation for mixed data. This correlation structure is then incorporated into a popular causal discovery algorithm, the pc algorithm, to identify causal structures. The proposed algorithm, called the latent-pc algorithm, is able to discover the true causal structure consistently under mild conditions in high dimensional settings. From simulation studies, the latent-pc algorithm delivers a competitive performance in terms of a similar or higher true positive rate and a similar or lower false positive rate, compared with other variants of the pc algorithm. In the high dimensional settings where the number of variables is more than the number of observations, the causal graphs identified by the latent-pc algorithm are closer to the true causal structures, compared to other competing algorithms. Further, we demonstrate the utility of the latent-pc algorithm in a real dataset for hepatocellular carcinoma. Causal structures for patient survival are visualized and connected with clinical interpretations in the literature.

关键词： causal discoveries latent Gaussian model mixed data pc algorithm rank correlation

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Contemporaneous causality among one hundred Chinese cities

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EMPIRICAL ECONOMICS 2022年第4期63卷 2315-2329页

作者： Xu, Xiaojie Zhang, Yun North Carolina State Univ Raleigh NC 27695 USA

This study explores dynamic relationships among Chinese housing prices for the years 2010-2019. With monthly data from 99 major cities in China, we use the vector error correction model and directed acyclic graph to characterize contemporaneous causality among housing prices from different tiers of cities. The pc algorithm identifies the causal pattern and the LiNGAM algorithm further identifies the causal path, from which we perform innovation accounting analysis. Complex housing price dynamics are found in the price adjustment process following price shocks, which is not only dominated by the top tiers of cities. This suggests that policies on housing prices in the long run might need to be planned from a national perspective.

关键词： Housing price Vector error correction model Directed acyclic graph Linear non-Gaussian acyclic model pc algorithm

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GPU Acceleration for Information-theoretic Constraint-based Causal Discovery

GPU Acceleration for Information-theoretic Constraint-based ...

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ACM SIGKDD Workshop on Causal Discovery (CD)

作者： Hagedorn, Christopher Lange, Constantin Huegle, Johannes Schlosser, Rainer Univ Potsdam Enterprise Platform & Integrat Concepts Hasso Plattner Inst Potsdam Germany

The discovery of causal relationships from observational data is an omnipresent task in data science. In real-world scenarios, observational data is often high-dimensional, and functional causal relationships can be nonlinear. To handle nonlinear relationships within constraint-based causal discovery, appropriate conditional independence tests (CI-tests) become necessary, e.g., non-parametric information-theory-based CI-tests. Both high-dimensional data and CI-tests for nonlinear relationships pose computational challenges. Existing work proposes parallel processing on Graphics Processing Units (GPUs) to address the computational demand resulting from high-dimensional data, in the case of discrete data or linear relationships. We extend this idea to cover CI-tests for nonlinear relationships in our work. Therefore, we develop GPUC(MIknn), a GPU-accelerated version of an existing CI-test, which builds upon conditional mutual information (CMI) combined with a local permutation scheme. Further, we propose a version of the pc algorithm, called GPUC(MIknn)-Parallel, to process multiple instances of GPU(CMIknn) on the GPU in parallel. Experiments show that the performance of GPU(CMIknn) is mainly affected by the number of k-nearest-neighbors (knn) within the CMI estimation. Depending on the chosen number of knn, the achieved speedup of GPU(CMIknn) ranges between factors of 2.3 to 352. In causal discovery, our method GPU(CMIknn)-Parallel outperforms a single-threaded CPU version by factors of up to 1 000, a multi-threaded CPU version using eight cores by factors of up to 240, and a naive GPU version by up to a factor 3.

关键词： GPU Acceleration pc algorithm Conditional Independence Testing High-dimensional Data

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