Estimation of causal effects with small data in the presence of trapdoor variables
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
© 2021 The Authors. Journal of the Royal Statistical Society: Series A (Statistics in Society) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society
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.
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Related funder(s)Academy of Finland
Funding program(s)Research profiles, AoF
Additional information about fundingAcademy of Finland, Grant/Award Number: 311877
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