Estimating Heterogeneity in Travel Mode Choice Shifts with Causal Forests
利用因果森林估计出行方式转变的异质性AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要以因果森林估计疫情相关的异质性效应，结果变量为汽车、公交和步行等离散出行选择，对应因果与离散选择。
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利用因果森林估计出行方式转变的异质性
- 研究问题
- 疫情前后汽车、公共交通与步行选择如何变化，这些变化在不同人口和行程特征之间有何异质性，并在什么条件下可作因果解释？
- 方法与思路
- 作者使用全国住户出行调查两个年份的行程记录，将疫情前年份作为对照、疫情后年份作为处理，利用因果森林估计平均及条件平均效应。
- 材料陈述的贡献
- 摘要报告汽车占比上升、公交和步行占比下降，并指出短途、高收入家庭及女性的汽车使用增幅较大，展示出行效应异质性的分析。
- 阅读关注点
- 应核对跨年份处理定义、同期变化与样本构成差异的控制、调查权重和行程依赖，以及因果森林的重叠、调参与区间估计设置。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验。年份比较的识别假设摘要未说明，使用因果森林本身不能确保疫情因果解释。
原文摘要
Objectives: While causal analysis of travel behavior is an emerging field, estimating heterogeneity in mode choice through causal modeling remains unexplored. This study demonstrates the application of a novel causal method, causal forest, to quantify the heterogeneity in travel mode choice shifts caused by the COVID-19 pandemic. Methods: We applied causal forests, a non-parametric causal machine learning method, to 802,935 trip records from the 2017 and 2022 waves of the National Household Travel Survey. The 2017 wave serves as the pre-pandemic control group, while the 2022 wave represents the treatment condition. Within the potential outcomes framework, we estimate average treatment effects (ATE), heterogeneous treatment effects (HTE), and conditional average treatment effects (CATE) across diverse socio-demographic groups and trip characteristics. Findings: Our results reveal an estimated ATE of a 1.86 percentage point (pp) increase in car-mode share, contrasted with decreases of 0.38 pp and 1.57 pp in public transit and walking, respectively. The largest increases in car use appeared for short-distance trips (one mile or less), households with annual incomes exceeding USD 200,000, and female travelers. Novelty: This is one of the first applications of causal forests to travel mode choice, and the first to use causal machine learning to estimate the pandemic's causal effect on mode choice analysis. Practical Applications: This study discusses methodological advantages, inherent assumptions, and limitations of causal forests within the context of transportation planning. This methodology is applied to COVID-19 travel data to illustrate how causal heterogeneity analysis can offer a deeper understanding of changes in mode choice. These insights are valuable for planners and policymakers in making policies related to mode shifts under an intervention.