Towards Causal Relationship in Indefinite Data: Baseline Model and New Datasets

Integrating deep learning and causal discovery has encouraged us to spot that learning causal structures and representations in dialogue and video is full of challenges. We defined These data forms as "Indefinite Data", characterized by multi-structure data and multi-value representations. Unlike existing adaptable data forms, Indefinite Data still faces gaps in datasets and methods. To address the dataset gap, we release two high-quality datasets - Causalogue and Causaction, containing text dialogue samples and video action samples with causal annotations respectively. Moreover, the method gap arises from the coexistence of multi-structure data and multi-value representations, breaking the assumptions of all current methods and rendering them infeasible on Indefinite Data. To this end, we propose a probabilistic framework as a baseline, incorporating three designed highlights for this gap: 1) establishing Causation Condition of representations using the independence of noise terms under non-fixed causal structures, 2) treating causal strength as a latent variable and measuring the reconstruction loss in the correlation space, and 3) estimating the effects of latent confounders. These highpoints make the probabilistic model capable of overcoming challenges brought by the coexistence of multi-structure data and multi-value representations and pave the way for the extension of latent confounders. Comprehensive experiments have evaluated baseline results of causal structures, causal representations, and confounding disentanglement.

CASR: Refining Action Segmentation via Magrinalizing Frame-levle Causal Relationships

Integrating deep learning and causal discovery has increased the interpretability of Temporal Action Segmentation (TAS) tasks. However, frame-level causal relationships exist many complicated noises outside the segment-level, making it infeasible to directly express macro action semantics. Thus, we propose Causal Abstraction Segmentation Refiner (CASR), which can refine TAS results from various models by enhancing video causality in marginalizing frame-level casual relationships. Specifically, we define the equivalent frame-level casual model and segment-level causal model, so that the causal adjacency matrix constructed from marginalized frame-level causal relationships has the ability to represent the segmnet-level causal relationships. CASR works out by reducing the difference in the causal adjacency matrix between we constructed and pre-segmentation results of backbone models. In addition, we propose a novel evaluation metric Causal Edit Distance (CED) to evaluate the causal interpretability. Extensive experimental results on mainstream datasets indicate that CASR significantly surpasses existing various methods in action segmentation performance, as well as in causal explainability and generalization.

A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations

The fusion of causal models with deep learning introducing increasingly intricate data sets, such as the causal associations within images or between textual components, has surfaced as a focal research area. Nonetheless, the broadening of original causal concepts and theories to such complex, non-statistical data has been met with serious challenges. In response, our study proposes redefinitions of causal data into three distinct categories from the standpoint of causal structure and representation: definite data, semi-definite data, and indefinite data. Definite data chiefly pertains to statistical data used in conventional causal scenarios, while semi-definite data refers to a spectrum of data formats germane to deep learning, including time-series, images, text, and others. Indefinite data is an emergent research sphere inferred from the progression of data forms by us. To comprehensively present these three data paradigms, we elaborate on their formal definitions, differences manifested in datasets, resolution pathways, and development of research. We summarize key tasks and achievements pertaining to definite and semi-definite data from myriad research undertakings, present a roadmap for indefinite data, beginning with its current research conundrums. Lastly, we classify and scrutinize the key datasets presently utilized within these three paradigms.

SSL Framework for Causal Inconsistency between Structures and Representations

The cross-pollination of deep learning and causal discovery has catalyzed a burgeoning field of research seeking to elucidate causal relationships within non-statistical data forms like images, videos, and text. Such data, often being named `indefinite data', exhibit unique challenges-inconsistency between causal structure and representation, which are not common in conventional data forms. To tackle this issue, we theoretically develop intervention strategies suitable for indefinite data and derive causal consistency condition (CCC). Moreover, we design a self-supervised learning (SSL) framework that considers interventions as `views' and CCC as a `philosophy' with two implement examples on Supervised Specialized Models (SSMs) and Large Language Models (LLMs), respectively. To evaluate pure inconsistency manifestations, we have prepared the first high-quality causal dialogue dataset-Causalogue. Evaluations are also performed on three other downstream tasks. Extensive experimentation has substantiated the efficacy of our methodology, illuminating how CCC could potentially play an influential role in various fields.

A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms

Understanding causality helps to structure interventions to achieve specific goals and enables predictions under interventions. With the growing importance of learning causal relationships, causal discovery tasks have transitioned from using traditional methods to infer potential causal structures from observational data to the field of pattern recognition involved in deep learning. The rapid accumulation of massive data promotes the emergence of causal search methods with brilliant scalability. Existing summaries of causal discovery methods mainly focus on traditional methods based on constraints, scores and FCMs, there is a lack of perfect sorting and elaboration for deep learning-based methods, also lacking some considers and exploration of causal discovery methods from the perspective of variable paradigms. Therefore, we divide the possible causal discovery tasks into three types according to the variable paradigm and give the definitions of the three tasks respectively, define and instantiate the relevant datasets for each task and the final causal model constructed at the same time, then reviews the main existing causal discovery methods for different tasks. Finally, we propose some roadmaps from different perspectives for the current research gaps in the field of causal discovery and point out future research directions.