Data visualization and causal reasoning are essential for causal effect estimation

Abstract Causal effect estimation has gained much attention in recent years, and it is not uncommon to see graduate students incorporate these analyses into their research. However, students may not be aware how model assumptions and causal reasoning can critically influence their results. We borrowed a simple dataset that was previously used to demonstrate the use of directed acyclic graphs and designed a set of accompanying data analysis multiple‐choice questions to engage students in inquiry‐based learning, guiding them to discover the importance of data visualization and subject‐specific causal reasoning for assessing different methods of causal effect estimation.