13 citations · 24 across the 15 of their papers we have counts for
18 papers
Causally Fair Generation with Large Language Models
Patrik Okanovic, Torsten Hoefler, Drago Plecko
Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape consequential decisions, raising conce…
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…
Interaction Testing in Variation Analysis
Drago Plecko
Relationships of cause and effect are of prime importance for explaining scientific phenomena. Often, rather than just understanding the effects of causes, researchers also wish to…
Fairness-Accuracy Trade-Offs: A Causal Perspective
Drago Plecko, Elias Bareinboim
Systems based on machine learning may exhibit discriminatory behavior based on sensitive characteristics such as gender, sex, religion, or race. In light of this, various notions o…
Fair Clustering: A Causal Perspective
Fritz Bayer, Drago Plecko, Niko Beerenwinkel +1
Clustering algorithms may unintentionally propagate or intensify existing disparities, leading to unfair representations or biased decision-making. Current fair clustering methods…
A Causal Framework for Decomposing Spurious Variations
Drago Plecko, Elias Bareinboim
One of the fundamental challenges found throughout the data sciences is to explain why things happen in specific ways, or through which mechanisms a certain variable exerts inf…
Causal Fairness for Outcome Control
Drago Plecko, Elias Bareinboim
As society transitions towards an AI-based decision-making infrastructure, an ever-increasing number of decisions once under control of humans are now delegated to automated system…
Reconciling Predictive and Statistical Parity: A Causal Approach
Drago Plecko, Elias Bareinboim
Since the rise of fair machine learning as a critical field of inquiry, many different notions on how to quantify and measure discrimination have been proposed in the literature. S…
Causal Fairness Analysis
Drago Plecko, Elias Bareinboim
Decision-making systems based on AI and machine learning have been used throughout a wide range of real-world scenarios, including healthcare, law enforcement, education, and finan…
fairadapt: Causal Reasoning for Fair Data Pre-processing
Drago Plečko, Nicolas Bennett, Nicolai Meinshausen
Machine learning algorithms are useful for various predictions tasks, but they can also learn how to discriminate, based on gender, race or other sensitive attributes. This realiza…
ricu: R's Interface to Intensive Care Data
Nicolas Bennett, Drago Plečko, Ida-Fong Ukor +2
Providing computational infrastructure for handling diverse intensive care unit (ICU) datasets, the R package 'ricu' enables writing dataset-agnostic analysis code, thereby facilit…
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Michael Moor, Nicolas Bennet, Drago Plecko +5
Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathoge…
Fair Data Adaptation with Quantile Preservation
Drago Plečko, Nicolai Meinshausen
Fairness of classification and regression has received much attention recently and various, partially non-compatible, criteria have been proposed. The fairness criteria can be enfo…