The inverse probability weighting (IPW) estimator is widely used for estimating the average treatment effect (ATE) in causal inference. However, the IPW estimator is prone to bias when the exposure variable is misclassified, even if the misclassification is non-differential. In this paper, we propose a correction for the IPW estimator using the method of moments (MoM) to account for misclassified exposure. We derive the corrected IPW estimator, demonstrate its unbiasedness under misclassification, and evaluate its performance through simulation studies. Furthermore, we discuss techniques for estimating misclassification probabilities, including scenarios with and without validation data or exposure replication. We also derive ranges of these probabilities for conducting sensitivity analyses when no gold standard is available. Additionally, we apply the proposed method to real-world data from the Bangladesh Multiple Indicator Cluster Survey (MICS) 2019 to estimate the causal effect of the wealth index on ICT skills among women aged 15-49, demonstrating the robustness of our method.
