Effect Prediction

Effect prediction research focuses on accurately forecasting the impact of interventions or variables on an outcome, emphasizing both prediction accuracy and explainability. Current work explores various machine learning models, including graph neural networks, variational autoencoders, and tree-based ensembles, often incorporating causal inference frameworks and knowledge graphs to improve robustness and interpretability. This field is crucial for diverse applications, from optimizing marketing campaigns and improving weather forecasting to enhancing medical treatments and addressing societal challenges like wealth inequality, by providing more reliable and understandable predictions of complex systems. The development of statistically sound methods for handling correlated features and mixed data types remains a key challenge.

Papers

October 11, 2024

Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction
Avishek Bose, Guojing Cong
Graph Neural Network Molecular Graph Drug Feature Drug Target Multilabel Classification Side Effect Effect Prediction

August 1, 2024

An effect analysis of the balancing techniques on the counterfactual explanations of student success prediction models
Mustafa Cavus, Jakub Kuzilek
Counterfactual Explanation Barzilai Borwein Technique Counterfactual Generation Student Performance Prediction Effect Prediction Counterfactual Generation Method

July 29, 2024

Causal Interventional Prediction System for Robust and Explainable Effect Forecasting
Zhixuan Chu, Hui Ding, Guang Zeng, Shiyu Wang, Yiming Li
Robust Version Causal Graph AI System Causal Prediction Effect Prediction

May 22, 2024

Wealth inequality and utility: Effect evaluation of redistribution and consumption morals using macro-econophysical coupled approach
Takeshi Kato, Mohammad Rezoanul Hoque
Task Utility Consumer Behavior Soft Inequality Effect Prediction Redistribution Mechanism Wealth Inequality

May 6, 2024

Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain
Malte Luttermann, Edgar Baake, Juljan Bouchagiar, Benjamin Gebel, Philipp Grüning, Dilini Manikwadura, Franziska Schollemann, Elisa Teifke, Philipp Rostalski, Ralf Möller
Markov Decision Process Medical Domain Accurate Treatment Failure Mode Effect Prediction Formula Based Computation

March 26, 2024

Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation
Hyeon-Ju Jeon, Jeon-Ho Kang, In-Hyuk Kwon, O-Joun Lee
Graph Neural Network Numerical Weather Prediction Observational Data Effect Prediction

February 5, 2024

Challenges in Variable Importance Ranking Under Correlation
Annie Liang, Thomas Jemielita, Andy Liaw, Vladimir Svetnik, Lingkang Huang, Richard Baumgartner, Jason M. Klusowski
Technical Challenge Total Correlation Feature Correlation Variable Importance Effect Prediction Joint Importance

December 31, 2022

NetEffect: Discovery and Exploitation of Generalized Network Effects
Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, Christos Faloutsos
Node Classification Scientific Discovery Real World Graph Real World eXploitation Effect Prediction Network Effect

October 28, 2022

Understanding Adverse Biological Effect Predictions Using Knowledge Graphs
Erik Bryhn Myklebust, Ernesto Jimenez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen
Knowledge Graph Extrapolation Framework Side Effect Effect Prediction

October 6, 2022

Conditional Feature Importance for Mixed Data
Kristin Blesch, David S. Watson, Marvin N. Wright
Feature Importance Marginal Feature Mixed Data Effect Prediction

August 22, 2022

Hierarchical Capsule Prediction Network for Marketing Campaigns Effect
Zhixuan Chu, Hui Ding, Guang Zeng, Yuchen Huang, Tan Yan, Yulin Kang, Sheng Li
Capsule Network Effect Prediction Individual Prediction

June 25, 2022

Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and Effect Classification
Carlos Fernández-Loría, Jorge Loría
New Framework Causal Effect Estimation Causal Ordering Effect Prediction Effect Estimation Causal Score

December 8, 2021

Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings
Erik B. Myklebust, Ernesto Jiménez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen
Knowledge Graph Human Prediction Knowledge Graph Embeddings Side Effect Chemical Property Prediction Effect Prediction Chemical Patent

Effect Prediction

Papers

Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction

An effect analysis of the balancing techniques on the counterfactual explanations of student success prediction models

Causal Interventional Prediction System for Robust and Explainable Effect Forecasting

Wealth inequality and utility: Effect evaluation of redistribution and consumption morals using macro-econophysical coupled approach

Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain

Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation

Challenges in Variable Importance Ranking Under Correlation

NetEffect: Discovery and Exploitation of Generalized Network Effects

Understanding Adverse Biological Effect Predictions Using Knowledge Graphs

Conditional Feature Importance for Mixed Data

Hierarchical Capsule Prediction Network for Marketing Campaigns Effect

Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and Effect Classification

Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings