Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to disco...
We have created a math learning environment with game-like elements such as narrative, visual feedback, personalization, collection, etc. We made a study with four different versio...
The causal notions embodied in the concept of Granger causality have been argued to belong to a different category than those of Judea Pearl’s Causal Model, and so far their re...
Structural causal models offer a popular framework for exploring causal concepts. However, due to their limited expressiveness, structural models have difficulties coping with su...
This paper describes an evolutionary method for identifying a causal model from the observed time series data. We use a system of ordinary differential equations (ODEs) as the cau...
The estimation of linear causal models (also known as structural equation models) from data is a well-known problem which has received much attention in the past. Most previous wo...
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations. However, modern datasets including gene expres...
: This paper presents a causal simulation method for incompletely known dynamic systems in process engineering. The causal model of a process is represented as both a causal networ...
Abstract. Causal modeling, such as noisy-OR, reduces probability parameters to be acquired in constructing a Bayesian network. Multiple causes can reinforce each other in producing...
The term “changes in structure,” originating from work in econometrics, refers to structural modifications invoked by actions on a causal model. In this paper we formalize the...