Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models
Abstract Panel data are ubiquitous in scientific domains such as sociology and econometrics. Various modeling approaches have been presented for the causal inference based on such data, including Markov models, cross-lagged panel models, and their extensions. Existing panel data modeling approaches typically impose some restrictive assumptions on the data-generating process, such as Gaussian responses, effects that are constant in time, and ability to consider only short-term causal effects.