How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study

Does X affect Y? Answering this question is particularly difficult if reverse causality is looming. Many social scientists turn to panel data to address such questions of causal ordering. Yet even in longitudinal analyses, reverse causality threatens causal inference based on conventional panel models. Whereas the methodological literature has suggested various alternative solutions, these approaches face many criticisms, chief among them to be sensitive to the correct specification of temporal lags. Applied researchers are thus left with little guidance. Seeking to provide such guidance, we compare how different panel models perform under a range of different conditions. Our Monte Carlo simulations reveal that unlike conventional panel models, a cross-lagged panel model with fixed effects not only offers protection against bias arising from reverse causality under a wide range of conditions but also helps to circumvent the problem of misspecified temporal lags.

Automated summary

This article discusses the issue of reverse causality in panel data analysis, highlighting the challenges it poses to causal inference based on conventional models. Although alternative solutions have been proposed, they have faced criticisms. Researchers need to find suitable panel models to address reverse causality and the issue of misspecified temporal lags.