Interventional Data

Interventional data, encompassing observations from experiments where specific variables are manipulated, is revolutionizing causal inference by supplementing observational data. Current research focuses on developing methods to effectively integrate interventional data into causal discovery algorithms, including those based on time series analysis, maximum entropy principles, and Bayesian networks, to learn more accurate and robust causal models. This enhanced ability to identify cause-and-effect relationships has significant implications across diverse fields, from robotics and healthcare to improving the fairness and efficiency of data-driven decision-making systems.

Papers

November 4, 2024

Linear Causal Bandits: Unknown Graph and Soft Interventions
Zirui Yan, Ali Tajer
Causal Graph Causal Bandit Unseen Graph Interventional Data Optimal Intervention Soft Intervention

October 31, 2024

Learning Mixtures of Unknown Causal Interventions
Abhinav Kumar, Kirankumar Shiragur, Caroline Uhler
Causal Graph Causal Direction Learning Mixture Markov Equivalence Class Interventional Data Node Intervention

October 3, 2024

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series
Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto
Causal Discovery Causal Structure Causal Model Causal Relationship Causal Analysis K Content Interventional Data

September 3, 2024

Estimating Joint interventional distributions from marginal interventional data
Sergio Hernan Garrido Mejia, Elke Kirschbaum, Armin Kekić, Atalanti Mastakouri
Maximum Entropy Causal Entropy Interventional Data Interventional Distribution

June 14, 2024

Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data
Ziyi Zhang, Shaogang Ren, Xiaoning Qian, Nick Duffield
Causal Structure Heterogeneous Treatment Effect Granger Causality Interventional Data

May 28, 2024

Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
Mathieu Chevalley, Patrick Schwab, Arash Mehrjou
Practical Algorithm Causal Inference Causal Structure Learning Formal Guarantee Causal Order Causal Ordering Interventional Data

May 2, 2024

IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning
Ryan Hoque, Ajay Mandlekar, Caelan Garrett, Ken Goldberg, Dieter Fox
Imitation Learning Robust Version Interventional Data

April 23, 2024

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
Peiwen Li, Xin Wang, Zeyang Zhang, Yuan Meng, Fang Shen, Yue Li, Jialong Wang, Yang Li, Wenweu Zhu
Large Language Model Causal Discovery Causal Discovery Method Interventional Data Temporal Causal Temporal Causal Discovery

December 1, 2023

Bayesian causal discovery from unknown general interventions
Alessandro Mascaro, Federico Castelletti
Causal Discovery Bayesian Inference Directed Acyclic Graph Markov Equivalence Class Interventional Data Bayesian Causal Discovery Unknown Intervention

November 10, 2023

Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting
Kaustav Bhattacharjee, Soumya Kundu, Indrasis Chakraborty, Aritra Dasgupta
Forecasting Model Interventional Data Visual Analytics Tool Net Load Forecasting

November 8, 2023

Learning Linear Gaussian Polytree Models with Interventions
D. Tramontano, L. Waldmann, M. Drton, E. Duarte
Causal Structure Health Intervention Interventional Data Equivalence Class

June 9, 2023

Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators
Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach
Causal Effect Estimation Linear Estimator Interventional Data

June 7, 2023

Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions
Himanshu Thakur, Atishay Jain, Praneetha Vaddamanu, Paul Pu Liang, Louis-Philippe Morency
Language Model Gender Bias Pre Trained Large Language Model Gender Information Language Modeling Societal Bias Interventional Data

May 30, 2023

dotears: Scalable, consistent DAG estimation using observational and interventional data
Albert Xue, Jingyou Rao, Sriram Sankararaman, Harold Pimentel
Potential Scalability Causal Structure Causal Direction Score Based Dot Product Regulatory Network DAG Learning Interventional Data

December 30, 2022

UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions
Asterios Bampakis, Sofia Yfantidou, Athena Vakali
End to End Data Driven Health Intervention Interventional Data Self Tracking

August 17, 2022

Modelling the long-term fairness dynamics of data-driven targeted help on job seekers
Sebastian Scher, Simone Kopeinik, Andreas Trügler, Dominik Kowald
Job Market Fairness Objective Long Term Fairness Fairness Issue Interventional Data Intervention Policy Personalized Knowledge

June 6, 2022

Is More Data All You Need? A Causal Exploration
Athanasios Vlontzos, Hadrien Reynaud, Bernhard Kainz
Medical Imaging Medical Image Datasets Causal Analysis Causal Approach Interventional Data

Interventional Data

Papers

Linear Causal Bandits: Unknown Graph and Soft Interventions

Learning Mixtures of Unknown Causal Interventions

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series

Estimating Joint interventional distributions from marginal interventional data

Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data

Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm

IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model

Bayesian causal discovery from unknown general interventions

Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

Learning Linear Gaussian Polytree Models with Interventions

Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators

Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions

dotears: Scalable, consistent DAG estimation using observational and interventional data

UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions

Modelling the long-term fairness dynamics of data-driven targeted help on job seekers

Is More Data All You Need? A Causal Exploration