no code implementations • 31 Mar 2024 • Niki Kiriakidou, Ioannis E. Livieris, Christos Diou
Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data.
1 code implementation • 11 Oct 2023 • Ioannis E. Livieris, Emmanuel Pintelas, Niki Kiriakidou, Panagiotis Pintelas
In this paper, we propose the concept of explainable image similarity, where the goal is the development of an approach, which is capable of providing similarity scores along with visual factual and counterfactual explanations.
no code implementations • 11 May 2023 • Niki Kiriakidou, Christos Diou
The proposed NNCI methodology is applied to some of the most well established neural network-based models for treatment effect estimation with the use of observational data.
no code implementations • 31 Aug 2022 • Niki Kiriakidou, Christos Diou
In this paper, we propose to complement the evaluation of causal inference models using concrete statistical evidence, including the performance profiles of Dolan and Mor{\'e}, as well as non-parametric and post-hoc statistical tests.
no code implementations • 23 May 2022 • Niki Kiriakidou, Christos Diou
Nowadays, in many scientific and industrial fields there is an increasing need for estimating treatment effects and answering causal questions.