arXiv:2412.06810v1 Announce Type: new
Abstract: Causal effect estimation under observational studies is challenging due to the lack of ground truth data and treatment assignment bias. Though various methods exist in literature for addressing this problem, most of them ignore multi-dimensional treatment information by considering it as scalar, either continuous or discrete. Recently, certain works have demonstrated the utility of this rich yet complex treatment information into the estimation process, resulting in better causal effect estimation. However, these works have been demonstrated on either graphs or textual treatments. There is a notable gap in existing literature in addressing higher dimensional data such as images that has a wide variety of applications. In this work, we propose a model named NICE (Network for Image treatments Causal effect Estimation), for estimating individual causal effects when treatments are images. NICE demonstrates an effective way to use the rich multidimensional information present in image treatments that helps in obtaining improved causal effect estimates. To evaluate the performance of NICE, we propose a novel semi-synthetic data simulation framework that generates potential outcomes when images serve as treatments. Empirical results on these datasets, under various setups including the zero-shot case, demonstrate that NICE significantly outperforms existing models that incorporate treatment information for causal effect estimation.
Expert Commentary
Estimating causal effects in observational studies is a challenging task due to the lack of ground truth data and treatment assignment bias. In this article, the authors highlight the limitations of existing methods that consider multi-dimensional treatment information as scalar, and propose a new model called NICE (Network for Image treatments Causal effect Estimation) to address this issue specifically for image treatments.
One of the key contributions of this work is incorporating rich, multidimensional information present in image treatments to improve causal effect estimation. While previous studies have mainly focused on graphs or textual treatments, the authors recognize the wide variety of applications that involve image treatments and aim to bridge the gap in the existing literature.
A multi-disciplinary approach is essential when dealing with image treatments as it requires knowledge from various domains such as computer vision, machine learning, and causal inference. The authors leverage techniques from these fields in developing NICE and demonstrate its effectiveness through empirical results.
Additionally, the authors propose a novel semi-synthetic data simulation framework to evaluate the performance of NICE. This framework generates potential outcomes when images are utilized as treatments, allowing for a comprehensive evaluation of the model under various scenarios, including the challenging zero-shot case.
The results from the experiments show that NICE outperforms existing models that incorporate treatment information for causal effect estimation. This highlights the importance of considering the multidimensional nature of image treatments and the potential improvements that can be achieved by leveraging this information effectively.
In conclusion, the proposed NICE model addresses the limitations of existing methods by incorporating rich multidimensional information in the estimation process for image treatments. The multi-disciplinary nature of this work, combining concepts from computer vision, machine learning, and causal inference, showcases the potential for advancements in causal effect estimation in various domains involving image treatments.