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.

The impact of income, land, and wealth inequality on agricultural expansion in Latin America

Agricultural expansion remains the most prominent proximate cause of tropical deforestation in Latin America, a region characterized by deforestation rates substantially above the world average and extremely high inequality. This paper deploys several multivariate statistical models to test whether different aspects of inequality, within a context of increasing agricultural productivity, promote agricultural expansion (Jevons paradox) or contraction (land-sparing) in 10 Latin American countries over 1990–2010. Here I show the existence of distinct patterns between the instantaneous and the overall (i.e., accounting for temporal lags) effect of increasing agricultural productivity, conditional on the degree of income, land, and wealth inequality. In a context of perfect equality, the instantaneous effect of increases in agricultural productivity is to promote agricultural expansion (Jevons paradox). When temporal lags are accounted for, agricultural productivity appears to be mainly land-sparing. Increases in the level of inequality, in all its forms, promote agricultural expansion, thus eroding the land-sparing effects of increasing productivity. The results also suggest that the instantaneous impact of inequality is larger than the overall effect (accounting for temporal lags) and that the effects of income inequality are stronger than those of land and wealth inequality, respectively. Reaping the benefits of increasing agricultural productivity, and achieving sustainable agricultural intensification in Latin America, requires policy interventions that specifically address inequality.

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.

Computer Control of Drug Delivery by Continuous Intravenous Infusion

Background: Intravenous drug infusion driven by syringe pumps may lead to substantial temporal lags in achieving steady-state delivery at target levels when using very low flow rates (“microinfusion”). This study evaluated computer algorithms for reducing temporal lags via coordinated control of drug and carrier flows. Methods: Novel computer control algorithms were developed based on mathematical models of fluid flow. Algorithm 1 controlled initiation of drug infusion and algorithm 2 controlled changes to ongoing steady-state infusions. These algorithms were tested in vitro and in vivo using typical high and low dead volume infusion system architectures. One syringe pump infused a carrier fluid and a second infused drug. Drug and carrier flowed together via a manifold through standard central venous catheters. Samples were collected in vitro for quantitative delivery analysis. Parameters including left ventricular max dP/dt were recorded in vivo. Results: Regulation by algorithm 1 reduced delivery delay in vitro during infusion initiation by 69% (low dead volume) and 78% (high dead volume). Algorithmic control in vivo measuring % change in max dP/dt showed similar results (55% for low dead volume and 64% for high dead volume). Algorithm 2 yielded greater precision in matching the magnitude and timing of intended changes in vivo and in vitro. Conclusions: Compared with conventional methods, algorithm-based computer control of carrier and drug flows can improve drug delivery by pump-driven intravenous infusion to better match intent. For norepinephrine infusions, the amount of drug reaching the bloodstream per time appears to be a dominant factor in the hemodynamic response to infusion.