IJMPR Didactic Paper: Weighting for Causal Inference in Mental Health Research

Abstract

Objective

Inverse probability weighting is a fundamental and general methodology for estimating the causal effects of exposures and interventions, but standard approaches to constructing such weights are often suboptimal.

Methods

In this paper, we describe a recent approach for constructing such weights that directly balances covariates while optimizing the stability of the resulting weighting estimator.

Results

To illustrate the use of this approach in mental health research, we present an exploratory study of the effects of exposure to violence on the risk of suicide attempt.

Conclusions

The direct balancing approach to weighting should be given strong consideration in empirical research due to its robustness and transparency in building weighting estimators.