Toward a Causal Interpretation from Observational Data: A New Bayesian Networks Method for Structural Models with Latent Variables

Abstract

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.

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 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.

The 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.

Keywords: causality; Bayesian networks; structural equation modeling; observational data; Bayesian graphs