Learning Why Things Change: The Difference-Based Causality Learner

03/15/2012
by   Mark Voortman, et al.
0

In this paper, we present the Difference- Based Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across time by means of difference equations driving change in a system. We motivate this representation with real-world mechanical systems and prove DBCL's correctness for learning structure from time series data, an endeavour that is complicated by the existence of latent derivatives that have to be detected. We also prove that, under common assumptions for causal discovery, DBCL will identify the presence or absence of feedback loops, making the model more useful for predicting the effects of manipulating variables when the system is in equilibrium. We argue analytically and show empirically the advantages of DBCL over vector autoregression (VAR) and Granger causality models as well as modified forms of Bayesian and constraintbased structure discovery algorithms. Finally, we show that our algorithm can discover causal directions of alpha rhythms in human brains from EEG data.

READ FULL TEXT
research
06/14/2020

Dynamic Window-level Granger Causality of Multi-channel Time Series

Granger causality method analyzes the time series causalities without bu...
research
12/29/2022

Investigating Sindy As a Tool For Causal Discovery In Time Series Signals

The SINDy algorithm has been successfully used to identify the governing...
research
05/10/2023

CUTS+: High-dimensional Causal Discovery from Irregular Time-series

Causal discovery in time-series is a fundamental problem in the machine ...
research
05/24/2018

Structure Learning from Time Series with False Discovery Control

We consider the Granger causal structure learning problem from time seri...
research
03/03/2021

D'ya like DAGs? A Survey on Structure Learning and Causal Discovery

Causal reasoning is a crucial part of science and human intelligence. In...
research
01/22/2014

Identifiability of an Integer Modular Acyclic Additive Noise Model and its Causal Structure Discovery

The notion of causality is used in many situations dealing with uncertai...
research
01/28/2021

Causality and independence in perfectly adapted dynamical systems

Perfect adaptation in a dynamical system is the phenomenon that one or m...

Please sign up or login with your details

Forgot password? Click here to reset