Event Causality Identification with Synthetic Control

• URL: http://arxiv.org/abs/2509.18156v1
• Date: Tue, 16 Sep 2025 22:05:52 GMT
• Title: Event Causality Identification with Synthetic Control
• Authors: Haoyu Wang, Fengze Liu, Jiayao Zhang, Dan Roth, Kyle Richardson,
• Abstract summary: Event causality identification (ECI) is crucial for distinguishing causation from correlation.<n>Traditional approaches to ECI have primarily utilized linguistic patterns and multi-hop relational inference.<n>In this paper, we adopt the Rubin Causal Model to identify event causality.
• Score: 48.18257618831177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Event causality identification (ECI), a process that extracts causal relations between events from text, is crucial for distinguishing causation from correlation. Traditional approaches to ECI have primarily utilized linguistic patterns and multi-hop relational inference, risking false causality identification due to informal usage of causality and specious graphical inference. In this paper, we adopt the Rubin Causal Model to identify event causality: given two temporally ordered events, we see the first event as the treatment and the second one as the observed outcome. Determining their causality involves manipulating the treatment and estimating the resultant change in the likelihood of the outcome. Given that it is only possible to implement manipulation conceptually in the text domain, as a work-around, we try to find a twin for the protagonist from existing corpora. This twin should have identical life experiences with the protagonist before the treatment but undergoes an intervention of treatment. However, the practical difficulty of locating such a match limits its feasibility. Addressing this issue, we use the synthetic control method to generate such a twin' from relevant historical data, leveraging text embedding synthesis and inversion techniques. This approach allows us to identify causal relations more robustly than previous methods, including GPT-4, which is demonstrated on a causality benchmark, COPES-hard.

Related papers

• Integrating Causal Reasoning into Automated Fact-Checking [0.0]
  In fact-checking applications, a common reason to reject a claim is to detect the presence of erroneous cause-effect relationships between the events at play.<n>We propose a methodology that combines event relation extraction, semantic similarity computation, and rule-based reasoning to detect logical inconsistencies between chains of events mentioned in a claim and in an evidence.
  arXiv  Detail & Related papers  (2025-12-15T12:56:00Z)
• CausalAffect: Causal Discovery for Facial Affective Understanding [26.904783570786773]
  CausalAffect is the first framework for causal graph discovery in facial affect analysis.<n>Our approach requires neither jointly annotated datasets nor handcrafted causal priors.<n>All trained models and source code will be released upon acceptance.
  arXiv  Detail & Related papers  (2025-11-29T12:07:33Z)
• Identifying while Learning for Document Event Causality Identification [19.44453370306568]
  Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document. Existing studies adopt a kind of identifying after learning paradigm, where events' representations are first learned and then used for the identification. We take care of the causal direction and propose a new identifying while learning mode for the ECI task.
  arXiv  Detail & Related papers  (2024-05-31T03:48:00Z)
• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Event Causality Is Key to Computational Story Understanding [5.167262076146664]
  We present the first method for event causality identification that leads to material improvements in computational story understanding. In the downstream story quality evaluation task, the identified causal relations lead to 3.6-16.6% relative improvement on correlation with human ratings. The findings indicate substantial untapped potential for event causality in computational story understanding.
  arXiv  Detail & Related papers  (2023-11-16T07:59:12Z)
• CLEVRER-Humans: Describing Physical and Causal Events the Human Way [51.68416979907198]
  We present the CLEVRER-Humans benchmark, a video dataset for causal judgment of physical events with human labels.<n>We employ two techniques to improve data collection efficiency: first, a novel iterative event cloze task to elicit a new representation of events in videos, which we term Causal Event Graphs (CEGs); second, a data augmentation technique based on neural language generative models.
  arXiv  Detail & Related papers  (2023-10-05T16:09:48Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning [87.92209048521153]
  Event temporal reasoning aims at identifying the temporal relations between two or more events from narratives. Knowledge conflicts arise when there is a mismatch between the actual temporal relations of events in the context and the prior knowledge or biases learned by the model.
  arXiv  Detail & Related papers  (2023-05-24T10:04:06Z)
• Event Causality Extraction with Event Argument Correlations [13.403222002600558]
  Event Causality Extraction aims to extract cause-effect event causality pairs from plain texts. We propose a method with a dual grid tagging scheme to capture the intra- and inter-event argument correlations for ECE.
  arXiv  Detail & Related papers  (2023-01-27T09:48:31Z)
• Active Bayesian Causal Inference [72.70593653185078]
  We propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning. ABCI jointly infers a posterior over causal models and queries of interest. We show that our approach is more data-efficient than several baselines that only focus on learning the full causal graph.
  arXiv  Detail & Related papers  (2022-06-04T22:38:57Z)
• Learning latent causal relationships in multiple time series [0.0]
  In many systems, the causal relations are embedded in a latent space that is expressed in the observed data as a linear mixture. A technique for blindly identifying the latent sources is presented. The proposed technique is unsupervised and can be readily applied to any multiple time series to shed light on the causal relationships underlying the data.
  arXiv  Detail & Related papers  (2022-03-21T00:20:06Z)
• Effect Identification in Cluster Causal Diagrams [51.42809552422494]
  We introduce a new type of graphical model called cluster causal diagrams (for short, C-DAGs) C-DAGs allow for the partial specification of relationships among variables based on limited prior knowledge. We develop the foundations and machinery for valid causal inferences over C-DAGs.
  arXiv  Detail & Related papers  (2022-02-22T21:27:31Z)
• Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement [17.77752074834281]
  We propose CauSeRL, which leverages external causal statements for event causality identification. First of all, we design a self-supervised framework to learn context-specific causal patterns from external causal statements. We adopt a contrastive transfer strategy to incorporate the learned context-specific causal patterns into the target ECI model.
  arXiv  Detail & Related papers  (2021-06-03T07:50:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.