Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation
This work addresses a crucial problem in causal inference for real-world applications with spatial-temporal data, offering a novel method that improves performance and generalization over classical statistical models.
The paper tackles the problem of estimating counterfactual outcomes with spatial-temporal attributes by proposing a Transformer-based framework, which shows stronger estimation capability than baseline methods in simulation experiments and is applied to analyze the causal effect of conflicts on forest loss in Colombia.
The real world naturally has dimensions of time and space. Therefore, estimating the counterfactual outcomes with spatial-temporal attributes is a crucial problem. However, previous methods are based on classical statistical models, which still have limitations in performance and generalization. This paper proposes a novel framework for estimating counterfactual outcomes with spatial-temporal attributes using the Transformer, exhibiting stronger estimation ability. Under mild assumptions, the proposed estimator within this framework is consistent and asymptotically normal. To validate the effectiveness of our approach, we conduct simulation experiments and real data experiments. Simulation experiments show that our estimator has a stronger estimation capability than baseline methods. Real data experiments provide a valuable conclusion to the causal effect of conflicts on forest loss in Colombia. The source code is available at https://github.com/lihe-maxsize/DeppSTCI_Release_Version-master.