Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models
Helske, J., & Tikka, S. (2024). Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models. Advances in Life Course Research, 60, Article 100617. https://doi.org/10.1016/j.alcr.2024.100617
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Advances in Life Course ResearchDate
2024Copyright
© 2024 The Author(s). Published by Elsevier Ltd.
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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ElsevierISSN Search the Publication Forum
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https://converis.jyu.fi/converis/portal/detail/Publication/213662371
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Research Council of FinlandFunding program(s)
Academy Project, AoFAdditional information about funding
This work was funded by the Research Council of Finland (decision numbers 331817, 355153, and 345546).License
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