Search Results for author: Valentyn Melnychuk

Found 12 papers, 8 papers with code

A Neural Framework for Generalized Causal Sensitivity Analysis

no code implementations27 Nov 2023 Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar

We provide theoretical guarantees that NeuralCSA is able to infer valid bounds on the causal query of interest and also demonstrate this empirically using both simulated and real-world data.

Causal Inference valid

Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation

no code implementations19 Nov 2023 Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel

In this paper, we propose a new, representation-agnostic framework for estimating bounds on the representation-induced confounding bias that comes from dimensionality reduction (or other constraints on the representations) in CATE estimation.

Dimensionality Reduction Representation Learning

Counterfactual Fairness for Predictions using Generative Adversarial Networks

no code implementations26 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.

Attribute counterfactual +2

Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model

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.

counterfactual Counterfactual Inference

Fair Off-Policy Learning from Observational Data

no code implementations15 Mar 2023 Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel

Algorithmic decision-making in practice must be fair for legal, ethical, and societal reasons.

Decision Making Fairness +1

Normalizing Flows for Interventional Density Estimation

1 code implementation13 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.

Causal Inference Density Estimation

Causal Transformer for Estimating Counterfactual Outcomes

1 code implementation14 Apr 2022 Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel

In this paper, we develop a novel Causal Transformer for estimating counterfactual outcomes over time.

counterfactual

Estimating average causal effects from patient trajectories

1 code implementation2 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.

Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels

1 code implementation23 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.

Retinal OCT Disease Classification Semi-Supervised Image Classification +1

Unsupervised Anomaly Detection for X-Ray Images

1 code implementation29 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.

Unsupervised Anomaly Detection

Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned

1 code implementation19 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.

Entity Alignment Knowledge Graphs

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