8 papers
Confounder Detection via Treatment Intent: A New Observational Study Design
Drago Plecko, Patrik Okanovic, Torsten Hoefler +1
Understanding the effects of interventions is central to scientific progress, with randomized controlled trials (RCTs) regarded as the gold standard for causal inference in many ap…
Causal Bias Detection in Generative Artificial Intelligence
Drago Plecko
Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical concerns about fairness and the perpetuation of demog…
Causal Algorithmic Recourse: Foundations and Methods
Drago Plecko, Collin Wang, Elias Bareinboim
The trustworthiness of AI decision-making systems is increasingly important. A key feature of such systems is the ability to provide recommendations for how an individual may rever…
Causal Fairness for Survival Analysis
Drago Plecko
In the data-driven era, large-scale datasets are routinely collected and analyzed using machine learning (ML) and artificial intelligence (AI) to inform decisions in high-stakes do…
Epidemiology of Large Language Models: A Benchmark for Observational Distribution Knowledge
Drago Plecko, Patrik Okanovic, Shreyas Havaldar +2
Artificial intelligence (AI) systems hold great promise for advancing various scientific disciplines, and are increasingly used in real-world applications. Despite their remarkable…
An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes
Drago Plecko, Paul Secombe, Andrea Clarke +7
The new era of large-scale data collection and analysis presents an opportunity for diagnosing and understanding the causes of health inequities. In this study, we describe a frame…