A1 Journal article (refereed)
Estimation of causal effects with small data in the presence of trapdoor variables (2021)


Helske, J., Tikka, S., & Karvanen, J. (2021). Estimation of causal effects with small data in the presence of trapdoor variables. Journal of the Royal Statistical Society. Series A: Statistics in Society, 184(3), 1030-1051. https://doi.org/10.1111/rssa.12699


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Publication details

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

Journal or series: Journal of the Royal Statistical Society. Series A: Statistics in Society

ISSN: 0964-1998

eISSN: 1467-985X

Publication year: 2021

Volume: 184

Issue number: 3

Pages range: 1030-1051

Publisher: Wiley-Blackwell

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1111/rssa.12699

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/75873

Publication is parallel published: https://arxiv.org/abs/2003.03187


Abstract

We consider the problem of estimating causal effects of interventions from observational data when well-known back-door and front-door adjustments are not applicable. We show that when an identifiable causal effect is subject to an implicit functional constraint that is not deducible from conditional independence relations, the estimator of the causal effect can exhibit bias in small samples. This bias is related to variables that we call trapdoor variables. We use simulated data to study different strategies to account for trapdoor variables and suggest how the related trapdoor bias might be minimized. The importance of trapdoor variables in causal effect estimation is illustrated with real data from the Life Course 1971–2002 study. Using this data set, we estimate the causal effect of education on income in the Finnish context. Bayesian modelling allows us to take the parameter uncertainty into account and to present the estimated causal effects as posterior distributions.


Keywords: Bayesian analysis; estimating; causality

Free keywords: Bayesian estimation; bias; causality; functional constraint; identifiability


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Last updated on 2021-20-09 at 15:40