While Large Language Models (LLMs) have been increasingly used to generate synthetic data for various downstream tasks, researchers overlook the causal direction in the data synthesis process. A natural causal direction should contain two steps: diverse raw data are generated first, and subsequently annotated for downstream tasks. However, most LLM-based methods adopt an anticausal direction: embedding label information in the prompt to force LLMs to generate targeted data. This reversal raises a critical question: How does the direction of data synthesis impact the quality and utility of the synthetic data? In this work, we empirically study the impact of causal and anticausal data synthesis. To do so, we first design simple yet effective prompting strategies to control the causal direction of LLM-based data synthesis. Using GPT-5 as the data generator, we construct synthetic datasets for three distinct machine learning tasks. We then fine-tune BERT-base and LLaMA-3.2-1B models on these datasets and evaluate them against human-curated benchmarks. Our experiments reveal consistent patterns: (1) models trained on anticausal synthetic data suffer larger performance drops across all tasks and model families --- Accuracy declines range from 13.7%-59.1% for BERT and 4.9%-54.3% for LLaMA, and (2) distributional analysis shows that anticausal synthetic datasets deviate further from human data. Our findings provide practical guidance on how to generate better synthetic data and make good use of it.

Recently, Light Field (LF) shows great potential in removing occlusion since the objects occluded in some views may be visible in other views. However, existing LF-based methods implicitly model each scene and can only remove objects that have positive disparities in one central views. In this article, we propose a novel Progressive Multi-Plane Images (MPI) Construction method specifically designed for LF-based occlusion removal. Different from the previous MPI construction methods, we progressively construct MPIs layer by layer in order from near to far. In order to accurately model the current layer, the positions of foreground occlusions in the nearer layers are taken as occlusion prior. Specifically, we propose an Occlusion-Aware Attention Network to generate each layer of MPIs with reliable information in occluded regions. For each layer, occlusions in the current layer are filtered out so that the background is better recovered just using the visible views instead of the other occluded views. Then, by simply removing the layers containing occlusions and rendering MPIs in kinds of viewpoints, the occlusion removal results for different views are generated. Experiments on synthetic and real-world scenes show that our method outperforms state-of-the-art LF occlusion removal methods in quantitative and visual comparisons. Moreover, we also apply the proposed progressive MPI construction method to the view synthesis task. The occlusion edges in our synthesized views achieve significantly better quality, which also verifies that our method can better model the occluded regions.

Causal reasoning plays a central role in human cognition. It facilitates the ability to infer, predict, and manipulate outcomes within the environment, which in turn lays the foundation for a uniquely adaptive decision-making framework that is crucial in navigating complex problem-solving contexts. With the pervasive influence of social media platforms, these online social networks have become critical for disseminating information, shaping public beliefs, and influencing daily life. However, no study has examined the propagation of causal language within social networks. In this work, we analyze the dispersion of messages containing causal language against those without, within the milieu of a large online social network. With the entirety of messages over one complete day on Twitter along with two additional days for validation, and with our validated ensemble method for identifying causal language, our findings reveal that messages with causal language exhibit a more extensive reach than those without. Furthermore, our counterfactual analysis demonstrates that the effect of causal language on information diffusion is truly causal. Moreover, our findings indicate that messages incorporating causal language manifest a higher ability to spread to out-groups compared to those without. These novel insights reveal the unique diffusion pattern of causal language within social networks, and suggest a potential to mitigate the echo chamber effect, while causal language could serve as a bridge for diverse perspectives.