Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration

10/04/2019
by   Erich Kummerfeld, et al.
0

Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice. Despite this, it is rarely used in any scientific or medical fields. One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a real-world setting. Trust is especially critical when human health is on the line. In this paper, we report the results of a series of simulation studies investigating the performance of different resampling methods as indicators of confidence in discovered graph features. We found that subsampling and sampling with replacement both performed surprisingly well, suggesting that they can serve as grounds for confidence in graph features. We also found that the calibration of subsampling and sampling with replacement had different convergence properties, suggesting that one's choice of which to use should depend on the sample size.

READ FULL TEXT

page 1

page 2

page 3

page 4

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
02/23/2022

Investigating the effect of binning on causal discovery

Binning (a.k.a. discretization) of numerically continuous measurements i...
research
05/17/2023

A Survey on Causal Discovery: Theory and Practice

Understanding the laws that govern a phenomenon is the core of scientifi...
research
06/15/2023

Bootstrap aggregation and confidence measures to improve time series causal discovery

Causal discovery methods have demonstrated the ability to identify the t...
research
03/15/2012

Causal Conclusions that Flip Repeatedly and Their Justification

Over the past two decades, several consistent procedures have been desig...
research
07/03/2020

High-recall causal discovery for autocorrelated time series with latent confounders

We present a new method for linear and nonlinear, lagged and contemporan...

Please sign up or login with your details

Forgot password? Click here to reset