1 code implementation • 27 Nov 2023 • Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
Unobserved confounding is common in many applications, making causal inference from observational data challenging.
1 code implementation • 19 Nov 2023 • Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
In this paper, we propose a new, representation-agnostic refutation framework for estimating bounds on the representation-induced confounding bias that comes from dimensionality reduction (or other constraints on the representations) in CATE estimation.
no code implementations • 26 Oct 2023 • Yuchen Ma, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
It is often achieved through counterfactual fairness, which ensures that the prediction for an individual is the same as that in a counterfactual world under a different sensitive attribute.
1 code implementation • 26 Oct 2023 • Konstantin Hess, Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
Treatment effect estimation in continuous time is crucial for personalized medicine.
1 code implementation • NeurIPS 2023 • Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
We further show that existing point counterfactual identification methods are special cases of our Curvature Sensitivity Model when the bound of the curvature is set to zero.
no code implementations • 15 Mar 2023 • Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
Algorithmic decision-making in practice must be fair for legal, ethical, and societal reasons.
1 code implementation • 13 Sep 2022 • Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
To the best of our knowledge, our Interventional Normalizing Flows are the first proper fully-parametric, deep learning method for density estimation of potential outcomes.
1 code implementation • 14 Apr 2022 • Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
In this paper, we develop a novel Causal Transformer for estimating counterfactual outcomes over time.
1 code implementation • 2 Mar 2022 • Dennis Frauen, Tobias Hatt, Valentyn Melnychuk, Stefan Feuerriegel
In medical practice, treatments are selected based on the expected causal effects on patient outcomes.
1 code implementation • 23 Oct 2020 • Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, Thomas Seidl
Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome unchanged.
Ranked #3 on Retinal OCT Disease Classification on OCT2017
Retinal OCT Disease Classification Semi-Supervised Image Classification +1
1 code implementation • 29 Jan 2020 • Diana Davletshina, Valentyn Melnychuk, Viet Tran, Hitansh Singla, Max Berrendorf, Evgeniy Faerman, Michael Fromm, Matthias Schubert
Therefore, we adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.
1 code implementation • 19 Nov 2019 • Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk, Volker Tresp, Thomas Seidl
In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task.
Ranked #33 on Entity Alignment on DBP15k zh-en