mlsanityData to 14 Oct 2026

Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation

arXiv 2506.21154 ↗First posted 26 Jun 2025stat.ME cs.AI cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 15 of its 45 references that are in the map. First posted on arXiv 26 Jun 2025.

Builds on

15 of 45 references.

References · 15

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  2. 2020Transformer Hawkes Processcited by 235
  3. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 155
  4. 2022Causal Transformer for Estimating Counterfactual Outcomescited by 123
  5. 2019Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounderscited by 75
  6. 2022Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equationscited by 59
  7. 2022From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answeringcited by 51
  8. 2021Neural Point Process for Learning Spatiotemporal Event Dynamicscited by 42
  9. 2020Estimating Individual Treatment Effects with Time-Varying Confounderscited by 21
  10. 2025Model Predictive Task Sampling for Efficient and Robust Adaptationcited by 21
  11. 2023Counterfactual Generative Models for Time-Varying Treatmentscited by 17
  12. 2020Causal Inference with Spatio-temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraqcited by 14
3 more
  1. 2020G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimescited by 13
  2. 2024Causal Contrastive Learning for Counterfactual Regression Over Timecited by 7
  3. 2018Bayesian method for causal inference in spatially-correlated multivariate time seriescited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Correlation or Causation: Analyzing the Causal Structures of LLM and LRM Reasoning Processcited by 3
  2. 2026SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysiscited by 1
  3. 2026PERO: Efficient Robust Post-Training Foundation Models for Encrypted Traffic Classificationcited by 0
  4. 2026Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modelingcited by 0