Fine Grained Causal

Fine-grained causal inference aims to identify precise causal relationships within complex systems, moving beyond broad correlations to uncover nuanced interactions. Current research focuses on developing methods to learn these relationships from diverse data types, including time series and high-dimensional datasets, employing techniques like Bayesian networks, deep generative models, and optimization-based approaches such as stochastic causal programming. These advancements are improving the accuracy of predictions in fields like climate modeling and reinforcement learning, while also enhancing our understanding of causal mechanisms in natural and social sciences, particularly in scenarios with unmeasured confounding. The ultimate goal is to build more robust and reliable models capable of handling intricate causal structures.

Papers

October 14, 2024

Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention
Ying Liu, Ge Bai, Chenji Lu, Shilong Li, Zhang Zhang, Ruifang Liu, Wenbin Guo
Visual Question Answering Visual Question Language Bias Context Bias Fine Grained Causal

August 8, 2024

Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
Dongqi Fu, Yada Zhu, Hanghang Tong, Kommy Weldemariam, Onkar Bhardwaj, Jingrui He
Anomaly Detection Causal Discovery State of the Art Forecasting Climate Data Granger Causality Neural Granger Fine Grained Causal

June 5, 2024

Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning
Inwoo Hwang, Yunhyeok Kwak, Suhyung Choi, Byoung-Tak Zhang, Sanghack Lee
Reinforcement Learning Native Robustness Causal Structure Quantization Operator Causal Dynamic Learning Fine Grained Causal

April 15, 2022

Towards Fine-grained Causal Reasoning and QA
Linyi Yang, Zhen Wang, Yuxiang Wu, Jie Yang, Yue Zhang
Yes No Question Causal Relation Cause Effect Causality Extraction Causality Detection Fine Grained Causal

February 22, 2022

Stochastic Causal Programming for Bounding Treatment Effects
Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner, Ricardo Silva, Niki Kilbertus
Causal Effect Causal Effect Estimation Fine Grained Causal

November 29, 2021

Encoding Causal Macrovariables
Benedikt Höltgen
High Dimensional Causal Relationship Climate Data Fine Grained Causal