Recently, interest has grown in the use of proxy variables of unobserved confounding for inferring the causal effect in the presence of unmeasured confounders from observational data. One difficulty inhibiting the practical use is finding valid proxy variables of unobserved confounding to a target causal effect of interest. These proxy variables are typically justified by background knowledge. In this paper, we investigate the estimation of causal effects among multiple treatments and a single outcome, all of which are affected by unmeasured confounders, within a linear causal model, without prior knowledge of the validity of proxy variables. To be more specific, we first extend the existing proxy variable estimator, originally addressing a single unmeasured confounder, to accommodate scenarios where multiple unmeasured confounders exist between the treatments and the outcome. Subsequently, we present two different sets of precise identifiability conditions for selecting valid proxy variables of unmeasured confounders, based on the second-order statistics and higher-order statistics of the data, respectively. Moreover, we propose two data-driven methods for the selection of proxy variables and for the unbiased estimation of causal effects. Theoretical analysis demonstrates the correctness of our proposed algorithms. Experimental results on both synthetic and real-world data show the effectiveness of the proposed approach.

Measuring public attitudes toward wildlife provides crucial insights into our relationship with nature and helps monitor progress toward Global Biodiversity Framework targets. Yet, conducting such assessments at a global scale is challenging. Manually curating search terms for querying news and social media is tedious, costly, and can lead to biased results. Raw news and social media data returned from queries are often cluttered with irrelevant content and syndicated articles. We aim to overcome these challenges by leveraging modern Natural Language Processing (NLP) tools. We introduce a folk taxonomy approach for improved search term generation and employ cosine similarity on Term Frequency-Inverse Document Frequency vectors to filter syndicated articles. We also introduce an extensible relevance filtering pipeline which uses unsupervised learning to reveal common topics, followed by an open-source zero-shot Large Language Model (LLM) to assign topics to news article titles, which are then used to assign relevance. Finally, we conduct sentiment, topic, and volume analyses on resulting data. We illustrate our methodology with a case study of news and X (formerly Twitter) data before and during the COVID-19 pandemic for various mammal taxa, including bats, pangolins, elephants, and gorillas. During the data collection period, up to 62% of articles including keywords pertaining to bats were deemed irrelevant to biodiversity, underscoring the importance of relevance filtering. At the pandemic's onset, we observed increased volume and a significant sentiment shift toward horseshoe bats, which were implicated in the pandemic, but not for other focal taxa. The proposed methods open the door to conservation practitioners applying modern and emerging NLP tools, including LLMs "out of the box," to analyze public perceptions of biodiversity during current events or campaigns.

This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. Given the labor-intensive nature of traditional qualitative analysis methods, such as coding, there exists a significant demand for tools that can facilitate the analysis process. Our research investigates various topic modeling techniques and concludes that the best model for analyzing interview texts is a combination of BERT embeddings and HDBSCAN clustering. We present a user-friendly software prototype that enables researchers, including those without programming skills, to efficiently process Figure 1: The coding process visualized and visualize the thematic structure of interview data. This tool not only facilitates the initial stages of qualitative analysis but also offers insights into the interconnectedness of topics revealed, thereby unwittingly faces the problem of interpretational objectivity, and enhancing the depth of qualitative analysis.

Benchmarking drug efficacy is a critical step in clinical trial design and planning. The challenge is that much of the data on efficacy endpoints is stored in scientific papers in free text form, so extraction of such data is currently a largely manual task. Our objective is to automate this task as much as possible. In this study we have developed and optimised a framework to extract efficacy endpoints from text in scientific papers, using a machine learning approach. Our machine learning model predicts 25 classes associated with efficacy endpoints and leads to high F1 scores (harmonic mean of precision and recall) of 96.4% on the test set, and 93.9% and 93.7% on two case studies. These methods were evaluated against - and showed strong agreement with - subject matter experts and show significant promise in the future of automating the extraction of clinical endpoints from free text. Clinical information extraction from text data is currently a laborious manual task which scales poorly and is prone to human error. Demonstrating the ability to extract efficacy endpoints automatically shows great promise for accelerating clinical trial design moving forwards.

Writing blog posts has become an integral part of many businesses' marketing strategies. But creating content that is engaging and optimized for search engines can be a time-consuming and complex task. That's where artificial intelligence (AI) can help. AI is quickly becoming an essential tool for automating and improving blog post writing. In this blog post, we'll explore how AI can be used to write blog posts, the pros, and cons of automating writing tasks with AI, the AI-powered writing tools available, and how to use AI to improve your blogging efficiency.

The artificial intelligence of Natural Language Processing (NLP) is concerned with how computers and people communicate in everyday language. In light of the deployment of NLP models in production systems, we need to streamline the rising use of NLP applications leading to MLOps (Machine Learning Operations) for NLP being helpful. Automating the creation, training, testing, and deployment of NLP models in production systems is the goal of MLOps for NLP. This article will examine the MLOps process for NLP models using Sentiment Analysis as a use case and some of the most recent trends and developments in this field. This article was published as a part of the Data Science Blogathon.

One reason deep learning exploded over the last decade was the availability of programming languages that could automate the math -- college-level calculus -- that is needed to train each new model. Neural networks are trained by tuning their parameters to try to maximize a score that can be rapidly calculated for training data. The equations used to adjust the parameters in each tuning step used to be derived painstakingly by hand. Deep learning platforms use a method called automatic differentiation to calculate the adjustments automatically. This allowed researchers to rapidly explore a huge space of models, and find the ones that really worked, without needing to know the underlying math.

AI is the process of programming a computer to make decisions for itself. In the insurance industry, AI automates many of the tasks currently done by human telemarketers. This will result in a more efficient and customer-friendly industry better equipped to compete in the digital age. This includes gathering data about their needs and preferences and their personal and financial information. This includes evaluating the customer's needs, choosing the best policy for them, and calculating the cost.

Artificial Intelligence is poised to have a massive impact on how people and businesses operate. It will transform industries from healthcare to transportation and retail. But it won't just affect things from your favorite apps to your day-to-day life. It's going to have a major impact on your company too. Think about the ways AI could help your company.

With the rise in consumption of short format videos and highly personalized content, have you ever thought of having a customized news video feed that would work based on your preferences? This kind of video feed would help us in avoiding redundant news and irrelevant content that we often consume from multiple sources. In this blog, let's make an attempt to automate the process of video creation. In the process, we will be using off the shelf pre-trained models (Fine-tuning these models would increase the performance though) for the sake of simplicity. First, let's select a text source on which we want to make a video on.