Causal Inference with Latent Variables: Recent Advances and Future Prospectives

Causality lays the foundation for the trajectory of our world. Causal inference (CI), which aims to infer intrinsic causal relations among variables of interest, has emerged as a crucial research topic. Nevertheless, the lack of observation of important variables (e.g., confounders, mediators, exogenous variables, etc.) severely compromises the reliability of CI methods. The issue may arise from the inherent difficulty in measuring the variables. Additionally, in observational studies where variables are passively recorded, certain covariates might be inadvertently omitted by the experimenter. Depending on the type of unobserved variables and the specific CI task, various consequences can be incurred if these latent variables are carelessly handled, such as biased estimation of causal effects, incomplete understanding of causal mechanisms, lack of individual-level causal consideration, etc. In this survey, we provide a comprehensive review of recent developments in CI with latent variables. We start by discussing traditional CI techniques when variables of interest are assumed to be fully observed. Afterward, under the taxonomy of circumvention and inference-based methods, we provide an in-depth discussion of various CI strategies to handle latent variables, covering the tasks of causal effect estimation, mediation analysis, counterfactual reasoning, and causal discovery. Furthermore, we generalize the discussion to graph data where interference among units may exist. Finally, we offer fresh aspects for further advancement of CI with latent variables, especially new opportunities in the era of large language models (LLMs).

UFID: A Unified Framework for Input-level Backdoor Detection on Diffusion Models

Diffusion Models are vulnerable to backdoor attacks, where malicious attackers inject backdoors by poisoning some parts of the training samples during the training stage. This poses a serious threat to the downstream users, who query the diffusion models through the API or directly download them from the internet. To mitigate the threat of backdoor attacks, there have been a plethora of investigations on backdoor detections. However, none of them designed a specialized backdoor detection method for diffusion models, rendering the area much under-explored. Moreover, these prior methods mainly focus on the traditional neural networks in the classification task, which cannot be adapted to the backdoor detections on the generative task easily. Additionally, most of the prior methods require white-box access to model weights and architectures, or the probability logits as additional information, which are not always practical. In this paper, we propose a Unified Framework for Input-level backdoor Detection (UFID) on the diffusion models, which is motivated by observations in the diffusion models and further validated with a theoretical causality analysis. Extensive experiments across different datasets on both conditional and unconditional diffusion models show that our method achieves a superb performance on detection effectiveness and run-time efficiency. The code is available at https://github.com/GuanZihan/official_UFID.

Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation

Geolocating precise locations from images presents a challenging problem in computer vision and information retrieval.Traditional methods typically employ either classification, which dividing the Earth surface into grid cells and classifying images accordingly, or retrieval, which identifying locations by matching images with a database of image-location pairs. However, classification-based approaches are limited by the cell size and cannot yield precise predictions, while retrieval-based systems usually suffer from poor search quality and inadequate coverage of the global landscape at varied scale and aggregation levels. To overcome these drawbacks, we present Img2Loc, a novel system that redefines image geolocalization as a text generation task. This is achieved using cutting-edge large multi-modality models like GPT4V or LLaVA with retrieval augmented generation. Img2Loc first employs CLIP-based representations to generate an image-based coordinate query database. It then uniquely combines query results with images itself, forming elaborate prompts customized for LMMs. When tested on benchmark datasets such as Im2GPS3k and YFCC4k, Img2Loc not only surpasses the performance of previous state-of-the-art models but does so without any model training.

Bridging Causal Discovery and Large Language Models: A Comprehensive Survey of Integrative Approaches and Future Directions

Causal discovery (CD) and Large Language Models (LLMs) represent two emerging fields of study with significant implications for artificial intelligence. Despite their distinct origins, CD focuses on uncovering cause-effect relationships from data, and LLMs on processing and generating humanlike text, the convergence of these domains offers novel insights and methodologies for understanding complex systems. This paper presents a comprehensive survey of the integration of LLMs, such as GPT4, into CD tasks. We systematically review and compare existing approaches that leverage LLMs for various CD tasks and highlight their innovative use of metadata and natural language to infer causal structures. Our analysis reveals the strengths and potential of LLMs in both enhancing traditional CD methods and as an imperfect expert, alongside the challenges and limitations inherent in current practices. Furthermore, we identify gaps in the literature and propose future research directions aimed at harnessing the full potential of LLMs in causality research. To our knowledge, this is the first survey to offer a unified and detailed examination of the synergy between LLMs and CD, setting the stage for future advancements in the field.

Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction

Since artificial intelligence has seen tremendous recent successes in many areas, it has sparked great interest in its potential for trustworthy and interpretable risk prediction. However, most models lack causal reasoning and struggle with class imbalance, leading to poor precision and recall. To address this, we propose a Task-Driven Causal Feature Distillation model (TDCFD) to transform original feature values into causal feature attributions for the specific risk prediction task. The causal feature attribution helps describe how much contribution the value of this feature can make to the risk prediction result. After the causal feature distillation, a deep neural network is applied to produce trustworthy prediction results with causal interpretability and high precision/recall. We evaluate the performance of our TDCFD method on several synthetic and real datasets, and the results demonstrate its superiority over the state-of-the-art methods regarding precision, recall, interpretability, and causality.

XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications

Artificial intelligence (AI), particularly machine learning and deep learning models, has significantly impacted bioinformatics research by offering powerful tools for analyzing complex biological data. However, the lack of interpretability and transparency of these models presents challenges in leveraging these models for deeper biological insights and for generating testable hypotheses. Explainable AI (XAI) has emerged as a promising solution to enhance the transparency and interpretability of AI models in bioinformatics. This review provides a comprehensive analysis of various XAI techniques and their applications across various bioinformatics domains including DNA, RNA, and protein sequence analysis, structural analysis, gene expression and genome analysis, and bioimaging analysis. We introduce the most pertinent machine learning and XAI methods, then discuss their diverse applications and address the current limitations of available XAI tools. By offering insights into XAI's potential and challenges, this review aims to facilitate its practical implementation in bioinformatics research and help researchers navigate the landscape of XAI tools.

PharmacyGPT: The AI Pharmacist

In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists. Our methodology encompasses the utilization of LLMs to generate comprehensible patient clusters, formulate medication plans, and forecast patient outcomes. We conduct our investigation using real data acquired from the intensive care unit (ICU) at the University of North Carolina Chapel Hill (UNC) Hospital. Our analysis offers valuable insights into the potential applications and limitations of LLMs in the field of clinical pharmacy, with implications for both patient care and the development of future AI-driven healthcare solutions. By evaluating the performance of PharmacyGPT, we aim to contribute to the ongoing discourse surrounding the integration of artificial intelligence in healthcare settings, ultimately promoting the responsible and efficacious use of such technologies.

Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference

Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-beta in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-beta accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-beta levels can impact AD development. In this paper, we propose a graph varying coefficient neural network (GVCNet) for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including GVCNet, for measuring the regional causal connections between amyloid-beta accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care.

BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks

Recently, the Segment Anything Model (SAM) has gained significant attention as an image segmentation foundation model due to its strong performance on various downstream tasks. However, it has been found that SAM does not always perform satisfactorily when faced with challenging downstream tasks. This has led downstream users to demand a customized SAM model that can be adapted to these downstream tasks. In this paper, we present BadSAM, the first backdoor attack on the image segmentation foundation model. Our preliminary experiments on the CAMO dataset demonstrate the effectiveness of BadSAM.

Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models

Recent advancements in foundation models (FMs), such as GPT-4 and LLaMA, have attracted significant attention due to their exceptional performance in zero-shot learning scenarios. Similarly, in the field of visual learning, models like Grounding DINO and the Segment Anything Model (SAM) have exhibited remarkable progress in open-set detection and instance segmentation tasks. It is undeniable that these FMs will profoundly impact a wide range of real-world visual learning tasks, ushering in a new paradigm shift for developing such models. In this study, we concentrate on the remote sensing domain, where the images are notably dissimilar from those in conventional scenarios. We developed a pipeline that leverages multiple FMs to facilitate remote sensing image semantic segmentation tasks guided by text prompt, which we denote as Text2Seg. The pipeline is benchmarked on several widely-used remote sensing datasets, and we present preliminary results to demonstrate its effectiveness. Through this work, we aim to provide insights into maximizing the applicability of visual FMs in specific contexts with minimal model tuning. The code is available at https://github.com/Douglas2Code/Text2Seg.