A1 Journal article (refereed)
Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models (2024)

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

JYU authors or editors

Helske, Jouni
Tikka, Santtu

Publication details

All authors or editors: Helske, Jouni; Tikka, Santtu

Journal or series: Advances in Life Course Research

ISSN: 1569-4909

eISSN: 1040-2608

Publication year: 2024

Publication date: 10/05/2024

Volume: 60

Article number: 100617

Publisher: Elsevier

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.alcr.2024.100617

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/95202

Publication is parallel published: https://doi.org/10.31235/osf.io/mdwu5

Abstract

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.

Keywords: Bayesian analysis; causality; Markov chains; panel survey; intervention

Free keywords: Bayesian methods; causal inference; Markov models; intervention; panel data; prediction

Fields of science: