109 citations · 212 across the 14 of their papers we have counts for
7 papers · 1 filter
gCastle: A Python Toolbox for Causal Discovery
Keli Zhang, Shengyu Zhu, Marcus Kalander +4
is an end-to-end Python toolbox for causal structure learning. It provides functionalities of generating data from either simulator or real-world dataset, learni…
Ordering-Based Causal Discovery with Reinforcement Learning
Xiaoqiang Wang, Yali Du, Shengyu Zhu +4
It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Recently, Reinforcement Learning (RL) has achieved promising result…
On Low Rank Directed Acyclic Graphs and Causal Structure Learning
Zhuangyan Fang, Shengyu Zhu, Jiji Zhang +3
Despite several advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the…
A Graph Autoencoder Approach to Causal Structure Learning
Ignavier Ng, Shengyu Zhu, Zhitang Chen +1
Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theor…
Masked Gradient-Based Causal Structure Learning
Ignavier Ng, Shengyu Zhu, Zhuangyan Fang +3
This paper studies the problem of learning causal structures from observational data. We reformulate the Structural Equation Model (SEM) with additive noises in a form parameterize…
Causal Discovery by Kernel Intrinsic Invariance Measure
Zhitang Chen, Shengyu Zhu, Yue Liu +1
Reasoning based on causality, instead of association has been considered as a key ingredient towards real machine intelligence. However, it is a challenging task to infer causal re…
Causal Discovery with Reinforcement Learning
Shengyu Zhu, Ignavier Ng, Zhitang Chen
Discovering causal structure among a set of variables is a fundamental problem in many empirical sciences. Traditional score-based casual discovery methods rely on various local he…