Instrumental Variables Two-Stage Least Squares (2SLS) vs. Maximum Likelihood Structural Equation Modeling of Causal Effects in Linear Regression Models |
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| Authors: | Alberto Maydeu-Olivares Dexin Shi Yves Rosseel |
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| Institution: | 1. University of South Carolina;2. University of Barcelonaamaydeu@sc.edu https://orcid.org/0000-0001-5790-392X;4. University of South Carolinahttps://orcid.org/0000-0002-4120-6756;5. University of Ghenthttps://orcid.org/0000-0002-4129-4477 |
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| Abstract: | In the presence of omitted variables or similar validity threats, regression estimates are biased. Unbiased estimates (the causal effects) can be obtained in large samples by fitting instead the Instrumental Variables Regression (IVR) model. The IVR model can be estimated using structural equation modeling (SEM) software or using Econometric estimators such as two-stage least squares (2SLS). We describe 2SLS using SEM terminology, and report a simulation study in which we generated data according to a regression model in the presence of omitted variables and fitted (a) a regression model using ordinary least squares, (b) an IVR model using maximum likelihood (ML) as implemented in SEM software, and (c) an IVR model using 2SLS. Coverage rates of the causal effect using regression methods are always unacceptably low (often 0). When using the IVR model, accurate coverage is obtained across all conditions when N = 500. Even when the IVR model is misspecified, better coverage than regression is generally obtained. Differences between 2SLS and ML are small and favor 2SLS in small samples (N ≤ 100). |
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| Keywords: | Causal inference structural equation modeling econometrics regression observational data |
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https://orcid.org/0000-0001-5790-392X;4. University of South Carolina