26 citations · 46 across the 11 of their papers we have counts for
11 papers
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets +2
Inferring unbiased treatment effects has received widespread attention in the machine learning community. In recent years, our community has proposed numerous solutions in standard…
DAGnosis: Localized Identification of Data Inconsistencies using Structures
Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat +3
Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models. While recent data-centric methods are able…
Learning Representations without Compositional Assumptions
Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian +1
This paper addresses unsupervised representation learning on tabular data containing multiple views generated by distinct sources of measurement. Traditional methods, which tackle…
Causal Deep Learning
Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian +1
Causality has the potential to truly transform the way we solve a large number of real-world problems. Yet, so far, its potential largely remains to be unlocked as causality often…
Navigating causal deep learning
Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian +1
Causal deep learning (CDL) is a new and important research area in the larger field of machine learning. With CDL, researchers aim to structure and encode causal knowledge in the e…
Differentiable and Transportable Structure Learning
Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie +1
Directed acyclic graphs (DAGs) encode a lot of information about a particular distribution in their structure. However, compute required to infer these structures is typically supe…
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
Tobias Hatt, Jeroen Berrevoets, Alicia Curth +2
Estimating heterogeneous treatment effects is an important problem across many domains. In order to accurately estimate such treatment effects, one typically relies on data from ob…
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
Jeroen Berrevoets, Fergus Imrie, Trent Kyono +2
Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missin…
Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time
Jeroen Berrevoets, Alicia Curth, Ioana Bica +2
Choosing the best treatment-plan for each individual patient requires accurate forecasts of their outcome trajectories as a function of the treatment, over time. While large observ…
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
Boris van Breugel, Trent Kyono, Jeroen Berrevoets +1
Machine learning models have been criticized for reflecting unfair biases in the training data. Instead of solving for this by introducing fair learning algorithms directly, we foc…
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects
Yao Zhang, Jeroen Berrevoets, Mihaela van der Schaar
Conditional average treatment effects (CATEs) allow us to understand the effect heterogeneity across a large population of individuals. However, typical CATE learners assume all co…