Credit, Kevin and Lehnert, Matthew (2023) A structured comparison of causal machine learning methods to assess heterogeneous treatment effects in spatial data. Journal of Geographical Systems. ISSN 1435-5930

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Abstract

The development of the “causal” forest by Wager and Athey (J Am Stat Assoc 113(523): 1228–1242, 2018) represents a significant advance in the area of explanatory/causal machine learning. However, this approach has not yet been widely applied to geographically referenced data, which present some unique issues: the random split of the test and training sets in the typical causal forest design fractures the spatial fabric of geographic data. To help solve this issue, we use a simulated dataset with known properties for average treatment effects and conditional average treatment effects to compare the performance of CF models across different definitions of the test/train split. We also develop a new “spatial” T-learner that can be implemented using predictive methods like random forest to provide estimates of heterogeneous treatment effects across all units. Our results show that all of the machine learning models outperform traditional ordinary least squares regression at identifying the true average treatment effect, but are not significantly different from one another. We then apply the preferred causal forest model in the context of analysing the treatment effect of the construction of the Valley Metro light rail (tram) system on on-road CO2 emissions per capita at the block group level in Maricopa County, Arizona, and find that the neighbourhoods most likely to benefit from treatment are those with higher pre-treatment proportions of transit and pedestrian commuting and lower proportions of auto commuting.

Item Type:	Article
Keywords:	Causal forest; Heterogeneous treatment effects; Machine learning; Causal inference; Spatial; CO2; emissions; Transit;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG Faculty of Social Sciences > Geography Faculty of Social Sciences > Research Institutes > Maynooth University Social Sciences Institute, MUSSI
Item ID:	18867
Identification Number:	https://doi.org/10.1007/s10109-023-00413-0
Depositing User:	Kevin Credit
Date Deposited:	12 Sep 2024 08:41
Journal or Publication Title:	Journal of Geographical Systems
Publisher:	Springer
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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