Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting confounding in multivariate linear models via spectral
  analysis   We study a model where one target variable Y is correlated with a vector
X:=(X_1,...,X_d) of predictor variables being potential causes of Y. We
describe a method that infers to what extent the statistical dependences
between X and Y are due to the influence of X on Y and to what extent due to a
hidden common cause (confounder) of X and Y. The method relies on concentration
of measure results for large dimensions d and an independence assumption
stating that, in the absence of confounding, the vector of regression
coefficients describing the influence of each X on Y typically has `generic
orientation' relative to the eigenspaces of the covariance matrix of X. For the
special case of a scalar confounder we show that confounding typically spoils
this generic orientation in a characteristic way that can be used to
quantitatively estimate the amount of confounding.

32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
1
The categories of this paper are: cs
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc