4 research outputs found
A Primer on Causal Analysis
We provide a conceptual map to navigate causal analysis problems. Focusing on
the case of discrete random variables, we consider the case of causal effect
estimation from observational data. The presented approaches apply also to
continuous variables, but the issue of estimation becomes more complex. We then
introduce the four schools of thought for causal analysisComment: Parts of this document are copied verbatim from Finnian Lattimore's
PhD thesis, ANU 201
Explaining Deep Learning Models using Causal Inference
Although deep learning models have been successfully applied to a variety of
tasks, due to the millions of parameters, they are becoming increasingly opaque
and complex. In order to establish trust for their widespread commercial use,
it is important to formalize a principled framework to reason over these
models. In this work, we use ideas from causal inference to describe a general
framework to reason over CNN models. Specifically, we build a Structural Causal
Model (SCM) as an abstraction over a specific aspect of the CNN. We also
formulate a method to quantitatively rank the filters of a convolution layer
according to their counterfactual importance. We illustrate our approach with
popular CNN architectures such as LeNet5, VGG19, and ResNet32
Using Unsupervised Learning to Help Discover the Causal Graph
The software outlined in this paper, AitiaExplorer, is an exploratory causal
analysis tool which uses unsupervised learning for feature selection in order
to expedite causal discovery. In this paper the problem space of causality is
briefly described and an overview of related research is provided. A problem
statement and requirements for the software are outlined. The key requirements
in the implementation, the key design decisions and the actual implementation
of AitiaExplorer are discussed. Finally, this implementation is evaluated in
terms of the problem statement and requirements outlined earlier. It is found
that AitiaExplorer meets these requirements and is a useful exploratory causal
analysis tool that automatically selects subsets of important features from a
dataset and creates causal graph candidates for review based on these features.
The software is available at https://github.com/corvideon/aitiaexplore
Genome-wide Causation Studies of Complex Diseases
Despite significant progress in dissecting the genetic architecture of
complex diseases by genome-wide association studies (GWAS), the signals
identified by association analysis may not have specific pathological relevance
to diseases so that a large fraction of disease causing genetic variants is
still hidden. Association is used to measure dependence between two variables
or two sets of variables. Genome-wide association studies test association
between a disease and SNPs (or other genetic variants) across the genome.
Association analysis may detect superficial patterns between disease and
genetic variants. Association signals provide limited information on the causal
mechanism of diseases. The use of association analysis as a major analytical
platform for genetic studies of complex diseases is a key issue that hampers
discovery of the mechanism of diseases, calling into question the ability of
GWAS to identify loci underlying diseases. It is time to move beyond
association analysis toward techniques enabling the discovery of the underlying
causal genetic strctures of complex diseases. To achieve this, we propose a
concept of a genome-wide causation studies (GWCS) as an alternative to GWAS and
develop additive noise models (ANMs) for genetic causation analysis. Type I
error rates and power of the ANMs to test for causation are presented. We
conduct GWCS of schizophrenia. Both simulation and real data analysis show that
the proportion of the overlapped association and causation signals is small.
Thus, we hope that our analysis will stimulate discussion of GWAS and GWCS.Comment: 61 pages, 5 figure