<b>Research Note</b>—Toward a Causal Interpretation from Observational Data: A New Bayesian Networks Method for Structural Models with Latent Variables
Authors: Zheng, Zhiqiang (Eric); Pavlou, Paul A.
Journal: Information Systems Research (2010)
<jats:p> 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. </jats:p><jats:p> Similar 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…