High Dimensional Causal

High-dimensional causal inference tackles the challenge of estimating causal effects when dealing with datasets containing numerous variables, such as images or complex time series. Current research focuses on adapting existing causal inference techniques, like backdoor adjustment and methods based on Granger causality, to these high-dimensional settings, often leveraging deep learning architectures such as generative models (e.g., normalizing flows) and graph neural networks to handle the complexity and high dimensionality. These advancements are crucial for addressing real-world problems in diverse fields, including medicine and economics, where high-dimensional data is prevalent and understanding causal relationships is essential for informed decision-making. The development of robust and scalable methods with finite-sample guarantees is a key area of ongoing investigation.

Papers

November 18, 2024

On the physics of nested Markov models: a generalized probabilistic theory perspective
Xingjian Zhang, Yuhao Wang
Causal Structure Causal Model Theoretical Physic High Dimensional Causal

January 2, 2024

Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
Md Musfiqur Rahman, Murat Kocaoglu
Causal Structure Causal Query Deep Causal Causal Generative Modular Learning Causal Hierarchy High Dimensional Causal

October 9, 2023

High Dimensional Causal Inference with Variational Backdoor Adjustment
Daniel Israel, Aditya Grover, Guy Van den Broeck
High Dimensional Causal Inference Variational Inference Backdoor Effect High Dimensional Causal

May 10, 2023

CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Yuxiao Cheng, Lianglong Li, Tingxiong Xiao, Zongren Li, Qin Zhong, Jinli Suo, Kunlun He
Causal Discovery Causal Graph Causal Discovery Method Causal Structure Learning Cut and Approximate Irregular Time Series High Dimensional Causal

March 1, 2023

Learning high-dimensional causal effect
Aayush Agarwal, Saksham Bassi
Synthetic Data Causal Effect Estimation Complex Deep High Dimensional Causal Synthetic Causal

September 13, 2022

Normalizing Flows for Interventional Density Estimation
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
Causal Inference Normalizing Flow Density Estimation High Dimensional Causal

June 15, 2022

Finite-Sample Guarantees for High-Dimensional DML
Victor Quintas-Martinez
Model Debiasing Finite Sample Guarantee Joint Inference Gaussian Approximation High Dimensional Causal

March 1, 2022

Neural Score Matching for High-Dimensional Causal Inference
Oscar Clivio, Fabian Falck, Brieuc Lehmann, George Deligiannidis, Chris Holmes
Causal Inference Score Matching Propensity Score High Dimensional Causal