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Found 5 papers, 1 papers with code
C-XGBoost: A tree boosting model for causal effect estimation
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.
Explainable Image Similarity: Integrating Siamese Networks and Grad-CAM
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.
Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation
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.
An evaluation framework for comparing causal inference models
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.
An improved neural network model for treatment effect estimation
Nowadays, in many scientific and industrial fields there is an increasing need for estimating treatment effects and answering causal questions.
