Interventional Distribution

Interventional distributions represent the probability distributions of variables after specific interventions, crucial for causal inference and decision-making under uncertainty. Current research focuses on estimating these distributions from observational and interventional data, employing methods like maximum entropy, sum-product networks (SPNs), and stochastic differential equations (SDEs), often incorporating neural networks for parameter estimation. This field is vital for advancing causal discovery, enabling fairer machine learning algorithms, and improving the generalization capabilities of models in diverse applications, including biomedical research and manufacturing. The development of robust methods for identifying and learning interventional distributions is key to building more reliable and interpretable AI systems.

Papers

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

August 14, 2024

$\chi$SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains
Harsh Poonia, Moritz Willig, Zhongjie Yu, Matej Zečević, Kristian Kersting, Devendra Singh Dhami
Causal Inference Sum Product Network Characteristic Function Interventional Distribution

October 30, 2023

Generator Identification for Linear SDEs with Additive and Multiplicative Noise
Yuanyuan Wang, Xi Geng, Wei Huang, Biwei Huang, Mingming Gong
Causal Inference Synchronous Generator Multiplicative Noise Parameter Identifiability Feed Additive Interventional Distribution

July 24, 2023

Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
Ludwig Bothmann, Susanne Dandl, Michael Schomaker
Machine Learning Causal Effect Interventional Distribution

July 7, 2023

When does the ID algorithm fail?
Ilya Shpitser
Causal Model Dynamic Feature Input Distribution Causal Graphical Model Interventional Distribution

June 15, 2023

Out of Distribution Generalization via Interventional Style Transfer in Single-Cell Microscopy
Wolfgang M. Pernice, Michael Doron, Alex Quach, Aditya Pratapa, Sultan Kenjeyev, Nicholas De Veaux, Michio Hirano, Juan C. Caicedo
Style Transfer Distribution Generalization Microscopy Image Interventional Distribution Single Cell Image

February 10, 2023

On the Interventional Kullback-Leibler Divergence
Jonas Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf
Causal Model Distributional Assumption Underlying Causal Causal Variable Interventional Distribution

January 14, 2023

Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions
Fabio Massimo Zennaro, Máté Drávucz, Geanina Apachitei, W. Dhammika Widanage, Theodoros Damoulas
Structural Causal Model Cognitive Abstraction Cause Effect Metric Aware Abstraction Causal Abstraction Interventional Distribution

March 4, 2022

Differentiable Causal Discovery Under Latent Interventions
Gonçalo R. A. Faria, André F. T. Martins, Mário A. T. Figueiredo
Variational Inference Causal Discovery Causal Graph Structural Causal Model Differentiable Causal Discovery Interventional Distribution