A1 Refereed original research article in a scientific journal
Estimating causal effects from panel data with dynamic multivariate panel models
Authors: Helske Jouni, Tikka Santtu
Publisher: Elsevier
Publication year: 2024
Journal: Advances in Life Course Research
Journal name in source: Advances in life course research
Journal acronym: Adv Life Course Res
Article number: 100617
Volume: 60
ISSN: 1569-4909
eISSN: 1879-6974
DOI: https://doi.org/10.1016/j.alcr.2024.100617
Web address : https://doi.org/10.1016/j.alcr.2024.100617
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/404679602
Panel data are ubiquitous in scientific fields such as social sciences. Various modeling approaches have been presented for observational causal inference based on such data. Existing approaches typically impose restrictive assumptions on the data-generating process such as Gaussian responses or time-invariant effects, or they can only consider short-term causal effects. To surmount these restrictions, we present the dynamic multivariate panel model (DMPM) that supports time-varying, time-invariant, and individual-specific effects, multiple responses across a wide variety of distributions, and arbitrary dependency structures of lagged responses of any order. We formally demonstrate how DMPM facilitates causal inference within the structural causal modeling framework and we take a Bayesian approach for the estimation of the posterior distributions of the model parameters and causal effects of interest. We demonstrate the use of DMPM by applying the approach to both real and synthetic data.
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