Estimating Network Causal Effects with Misclassified Outcomes: Evidence from Karnataka

Misclassification of binary outcomes in network settings may bias the estimates of causal effects, including spillover effects that arise from social interactions, and may generate spurious causal effects. To address this issue, we develop a parametric framework that jointly estimates misclassification probabilities and causal effect parameters within a binary choice model with neighborhood exposure mappings. Monte Carlo simulations show that ignoring outcome misclassification or network-related variables leads to substantial bias, whereas the proposed method achieves a smaller bias and RMSE. By applying the method to microfinance and social network data from Karnataka, we find that under binary exposure, ignoring outcome misclassification yields statistically significant spillover and overall effects, whereas these effects become statistically insignificant once outcome misclassification is corrected for. Furthermore, omitting network-related variables overstates the direct effect. These results underscore the importance of jointly correcting for outcome misclassification and accounting for network-related variables to obtain credible causal inference.