Learning latent causal graphs via mixture oracles

06/29/2021
by   Bohdan Kivva, et al.
0

We study the problem of reconstructing a causal graphical model from data in the presence of latent variables. The main problem of interest is recovering the causal structure over the latent variables while allowing for general, potentially nonlinear dependence between the variables. In many practical problems, the dependence between raw observations (e.g. pixels in an image) is much less relevant than the dependence between certain high-level, latent features (e.g. concepts or objects), and this is the setting of interest. We provide conditions under which both the latent representations and the underlying latent causal model are identifiable by a reduction to a mixture oracle. The proof is constructive, and leads to several algorithms for explicitly reconstructing the full graphical model. We discuss efficient algorithms and provide experiments illustrating the algorithms in practice.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/05/2023

Learning nonparametric latent causal graphs with unknown interventions

We establish conditions under which latent causal graphs are nonparametr...
research
10/19/2019

Measurement Dependence Inducing Latent Causal Models

We consider the task of causal structure learning over measurement depen...
research
10/11/2021

Learning Temporally Causal Latent Processes from General Temporal Data

Our goal is to recover time-delayed latent causal variables and identify...
research
01/31/2020

Causal Structure Discovery from Distributions Arising from Mixtures of DAGs

We consider distributions arising from a mixture of causal models, where...
research
05/27/2019

Ancestral causal learning in high dimensions with a human genome-wide application

We consider learning ancestral causal relationships in high dimensions. ...
research
01/19/2023

Causal conditional hidden Markov model for multimodal traffic prediction

Multimodal traffic flow can reflect the health of the transportation sys...
research
03/02/2023

Vine dependence graphs with latent variables as summaries for gene expression data

The advent of high-throughput sequencing technologies has lead to vast c...

Please sign up or login with your details

Forgot password? Click here to reset