From Correlation to Causation: Counterfactual Thinking using Police Stops Data for Undergraduate Students

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Calls to incorporate causal reasoning into undergraduate statistics education have grown in recent years, yet resources remain scarce. This paper presents a sequence of five activities designed to help students transition from correlational to causal reasoning using real-world police stops data. The paper’s contribution to statistics education lies in providing an implementable framework integrating practical data analysis, counterfactual reasoning through the Rubin Causal Model, and directed acyclic graphs, offering instructors a comprehensive way to introduce causal inference concepts in applied settings. The activities, which ask the question “Do police officers discriminate against Black drivers?”, were implemented in an undergraduate electives course on quantitative analysis of structural injustice. We used publicly available data, case-based discussion prompts, and scaffolded activities. The course enrolled upperclassmen who have completed prerequisites in regression modeling and R programming. Across 300 students between 2022 and 2025, the instructor’s observations and reflections support the idea that activities improve students’ distinguishing correlation from causation and assessing the limits of observational data. The course also enrolled underrepresented groups in statistics, fostering diverse opinions. Practices for introducing controversial topics in statistics education are discussed. The activity sequence discussed offers one adaptable model for integrating causal reasoning into statistics instruction.

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