Graphical criteria for efficient total effect estimation via adjustment in causal linear models
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Graphical criteria for efficient total effect estimation via adjustment in causal linear models. / Henckel, Leonard; Perković, Emilija; Maathuis, Marloes H.
In: Journal of the Royal Statistical Society. Series B: Statistical Methodology, Vol. 84, No. 2, 2022, p. 579-599.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Graphical criteria for efficient total effect estimation via adjustment in causal linear models
AU - Henckel, Leonard
AU - Perković, Emilija
AU - Maathuis, Marloes H.
N1 - Publisher Copyright: © 2022 The Authors. Journal of the Royal Statistical Society: Series B (StatisticalMethodology) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.
PY - 2022
Y1 - 2022
N2 - Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid adjustment sets, that is, all covariate sets that can be used for this purpose. Different valid adjustment sets typically provide total causal effect estimates of varying accuracies. Restricting ourselves to causal linear models, we introduce a graphical criterion to compare the asymptotic variances provided by certain valid adjustment sets. We employ this result to develop two further graphical tools. First, we introduce a simple variance decreasing pruning procedure for any given valid adjustment set. Second, we give a graphical characterization of a valid adjustment set that provides the optimal asymptotic variance among all valid adjustment sets. Our results depend only on the graphical structure and not on the specific error variances or edge coefficients of the underlying causal linear model. They can be applied to directed acyclic graphs (DAGs), completed partially directed acyclic graphs (CPDAGs) and maximally oriented partially directed acyclic graphs (maximal PDAGs). We present simulations and a real data example to support our results and show their practical applicability.
AB - Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid adjustment sets, that is, all covariate sets that can be used for this purpose. Different valid adjustment sets typically provide total causal effect estimates of varying accuracies. Restricting ourselves to causal linear models, we introduce a graphical criterion to compare the asymptotic variances provided by certain valid adjustment sets. We employ this result to develop two further graphical tools. First, we introduce a simple variance decreasing pruning procedure for any given valid adjustment set. Second, we give a graphical characterization of a valid adjustment set that provides the optimal asymptotic variance among all valid adjustment sets. Our results depend only on the graphical structure and not on the specific error variances or edge coefficients of the underlying causal linear model. They can be applied to directed acyclic graphs (DAGs), completed partially directed acyclic graphs (CPDAGs) and maximally oriented partially directed acyclic graphs (maximal PDAGs). We present simulations and a real data example to support our results and show their practical applicability.
KW - causal inference
KW - covariate adjustment
KW - efficiency
KW - graphical models
U2 - 10.1111/rssb.12451
DO - 10.1111/rssb.12451
M3 - Journal article
AN - SCOPUS:85126381370
VL - 84
SP - 579
EP - 599
JO - Journal of the Royal Statistical Society, Series B (Statistical Methodology)
JF - Journal of the Royal Statistical Society, Series B (Statistical Methodology)
SN - 1369-7412
IS - 2
ER -
ID: 342613741