In this paper, we focus on two causal inference tasks, i. e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task.
Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities.
The LGN is a GAN-based architecture which learns and samples from the causal model over labels.
The creation, dissemination, and consumption of disinformation and fabricated content on social media is a growing concern, especially with the ease of access to such sources, and the lack of awareness of the existence of such false information.
In this work, models that aim to answer causal questions are referred to as causal interpretable models.