Search Results for author: Jeroen Berrevoets

Found 12 papers, 5 papers with code

Differentiable and Transportable Structure Learning

no code implementations13 Jun 2022 Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

We are interested in unsupervised structure learning with a particular focus on directed acyclic graphical (DAG) models.

Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects

1 code implementation25 Feb 2022 Tobias Hatt, Jeroen Berrevoets, Alicia Curth, Stefan Feuerriegel, Mihaela van der Schaar

While observational data is confounded, randomized data is unconfounded, but its sample size is usually too small to learn heterogeneous treatment effects.

Representation Learning

To Impute or not to Impute? Missing Data in Treatment Effect Estimation

no code implementations4 Feb 2022 Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar

Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects.

Imputation

Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time

no code implementations7 Dec 2021 Jeroen Berrevoets, Alicia Curth, Ioana Bica, Eoin McKinney, Mihaela van der Schaar

Choosing the best treatment-plan for each individual patient requires accurate forecasts of their outcome trajectories as a function of the treatment, over time.

Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects

1 code implementation6 Aug 2021 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.

Dimensionality Reduction

HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks

no code implementations26 Aug 2020 Sam Verboven, Muhammad Hafeez Chaudhary, Jeroen Berrevoets, Wouter Verbeke

Multi-task learning (MTL) can improve performance on a task by sharing representations with one or more related auxiliary-tasks.

Multi-Task Learning

The foundations of cost-sensitive causal classification

no code implementations24 Jul 2020 Wouter Verbeke, Diego Olaya, Jeroen Berrevoets, Sam Verboven, Sebastián Maldonado

The framework is shown to instantiate to application-specific cost-sensitive performance measures that have been recently proposed for evaluating customer retention and response uplift models, and allows to maximize profitability when adopting a causal classification model for optimizing decision-making.

Classification Decision Making +1

Autoencoders for strategic decision support

no code implementations3 May 2020 Sam Verboven, Jeroen Berrevoets, Chris Wuytens, Bart Baesens, Wouter Verbeke

However, few data-driven tools that support strategic decision-making are available.

Decision Making

Optimising Individual-Treatment-Effect Using Bandits

1 code implementation16 Oct 2019 Jeroen Berrevoets, Sam Verboven, Wouter Verbeke

Applying causal inference models in areas such as economics, healthcare and marketing receives great interest from the machine learning community.

Causal Inference

Causal Simulations for Uplift Modeling

1 code implementation1 Feb 2019 Jeroen Berrevoets, Wouter Verbeke

Hence, methods are being developed that are able to learn from newly gained experience, as well as handle drifting environments.

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