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首页> 外文期刊>Information Systems Research >Toward a Causal Interpretation from Observational Data: A New Bayesian Networks Method for Structural Models with Latent Variables
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Toward a Causal Interpretation from Observational Data: A New Bayesian Networks Method for Structural Models with Latent Variables

机译：基于观测数据的因果解释：具有潜在变量的结构模型的新贝叶斯网络方法

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摘要

Because a fundamental attribute of a good theory is causality, the information systems (IS) literature has strived to infer causality from empirical data, typically seeking causal interpretations from longitudinal, experimental, and panel data that include time precedence. However, such data are not always obtainable and observational (cross-sectional, nonexperimental) data are often the only data available. To infer causality from observational data that are common in empirical IS research, this study develops a new data analysis method that integrates the Bayesian networks (BN) and structural equation modeling (SEM) literatures.rnSimilar to SEM techniques (e.g., LISREL and PLS), the proposed Bayesian networks for latent variables (BN-LV) method tests both the measurement model and the structural model. The method operates in two stages: First, it inductively identifies the most likely LVs from measurement items without prespecifying a measurement model. Second, it compares all the possible structural models among the identified LVs in an exploratory (automated) fashion and it discovers the most likely causal structure. By exploring the causal structural model that is not restricted to linear relationships, BN-LV contributes to the empirical IS literature by overcoming three SEM limitations (Lee, B., A. Barua, A. B. Whinston. 1997. Discovery and representation of causal relationships in MIS research: A methodological framework. MIS Quart. 21(1) 109-136)-lack of causality inference, restrictive model structure, and lack of nonlinearities. Moreover, BN-LV extends the BN literature by (1) overcoming the problem of latent variable identification using observed (raw) measurement items as the only inputs, and (2) enabling the use of ordinal and discrete (Likert-type) data, which are commonly used in empirical IS studies.rnThe BN-LV method is first illustrated and tested with actual empirical data to demonstrate how it can help reconcile competing hypotheses in terms of the direction of causality in a structural model. Second, we conduct a comprehensive simulation study to demonstrate the effectiveness of BN-LV compared to existing techniques in the SEM and BN literatures. The advantages of BN-LV in terms of measurement model construction and structural model discovery are discussed.
机译：因为好的理论的基本属性是因果关系，所以信息系统（IS）文献一直在努力从经验数据推断因果关系，通常是从包括时间优先权的纵向，实验和面板数据中寻求因果关系。但是，此类数据并非总是可获取的，并且观察（横截面，非实验）数据通常是唯一可用的数据。为了从经验主义IS研究中常见的观测数据中推断因果关系，本研究开发了一种新的数据分析方法，该方法将贝叶斯网络（BN）和结构方程模型（SEM）文献相结合。rn与SEM技术类似（例如LISREL和PLS） ，提出的贝叶斯潜在变量网络（BN-LV）方法同时测试了测量模型和结构模型。该方法分为两个阶段：首先，它无需预先指定测量模型即可从测量项目中归纳识别最可能的LV。其次，它以探索性（自动）方式比较已识别的左室之间的所有可能的结构模型，并发现最可能的因果结构。通过探索不限于线性关系的因果结构模型，BN-LV通过克服三个SEM限制（Lee，B.，A. Barua，AB Whinston。1997.在因果关系中的发现和表示），为经验主义IS文献做出了贡献。 MIS研究：一种方法框架（MIS Quart。21（1）109-136）-缺乏因果关系推论，模型结构受限且缺乏非线性。此外，BN-LV通过以下方式扩展了BN文献：（1）克服使用观测的（原始）测量项作为唯一输入的潜在变量识别问题，以及（2）允许使用序数和离散（Likert型）数据，首先，对BN-LV方法进行了举例说明，并用实际的经验数据进行了测试，以证明BN-LV方法如何在结构模型的因果关系方面帮助调和竞争假设。其次，我们进行了全面的模拟研究，以证明与SEM和BN文献中的现有技术相比，BN-LV的有效性。讨论了BN-LV在测量模型构建和结构模型发现方面的优势。

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