SummaryThis paper undertakes a replication in a wide sense of a recent study that examines the relationship between historical plough agriculture and current gender roles. We revisit the main research question with recently developed causal machine learning methods, which allow researchers to model the relationship of covariates with the treatment and the outcomes in a more flexible way, while also including interactions and nonlinearities that were not considered in the original analysis. Our results suggest an even larger negative effect of the historical plough adoption on female labor force participation than what the original analysis found. The paper highlights the benefits of using causal machine learning methods in applied empirical economics.