Variance estimation for average treatment effects estimated by g-computation

Stefan Nygaard Hansen () and Morten Overgaard ()
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Stefan Nygaard Hansen: Aarhus University
Morten Overgaard: Aarhus University

Metrika: International Journal for Theoretical and Applied Statistics, 2025, vol. 88, issue 4, No 1, 419-443

Abstract: Abstract The average treatment effect is used to evaluate effects of interventions in a population. Under certain causal assumptions, such an effect may be estimated from observational data using the g-computation technique. The asymptotic properties of this estimator appears not to be well-known and hence bootstrapping has become the preferred method for estimating its variance. Bootstrapping is, however, not an optimal choice for multiple reasons; it is a slow procedure and, if based on too few bootstrap samples, results in a highly variable estimator of the variance. In this paper, we consider estimators of potential outcome means and average treatment effects using g-computation. We consider these parameters for the entire population but also in subgroups, for example, the average treatment effect among the treated. We derive their asymptotic distributions in a general framework. An estimator of the asymptotic variance is proposed and shown to be consistent when g-computation is used in conjunction with the M-estimation technique. The proposed estimator is shown to be superior to the bootstrap technique in a simulation study. Robustness against model misspecification is also demonstrated by means of simulations.

Keywords: Average treatment effect; Causal inference; G-computation; Model misspecification; Variance estimation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00184-024-00962-4

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