A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat

Helske, Jouni
Tikka, Santtu

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Helske, Jouni; Tikka, Santtu

Lehti tai sarja: Advances in Life Course Research

ISSN: 1569-4909

eISSN: 1040-2608

Julkaisuvuosi: 2024

Ilmestymispäivä: 10.05.2024

Volyymi: 60

Artikkelinumero: 100617

Kustantaja: Elsevier

Julkaisumaa: Alankomaat

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/95202

Julkaisu on rinnakkaistallennettu: https://doi.org/10.31235/osf.io/mdwu5

Tiivistelmä

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.

YSO-asiasanat: bayesilainen menetelmä; kausaliteetti; Markovin ketjut; paneelitutkimus; interventio

Vapaat asiasanat: Bayesian methods; causal inference; Markov models; intervention; panel data; prediction

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