Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Gaussian process framework for overlap and causal effect estimation
  with high-dimensional covariates   A powerful tool for the analysis of nonrandomized observational studies has
been the potential outcomes model. Utilization of this framework allows
analysts to estimate average treatment effects. This article considers the
situation in which high-dimensional covariates are present and revisits the
standard assumptions made in causal inference. We show that by employing a
flexible Gaussian process framework, the assumption of strict overlap leads to
very restrictive assumptions about the distribution of covariates, results for
which can be characterized using classical results from Gaussian random
measures as well as reproducing kernel Hilbert space theory. In addition, we
propose a strategy for data-adaptive causal effect estimation that does not
rely on the strict overlap assumption. These findings reveal the stringency
that accompanies the use of the treatment positivity assumption in
high-dimensional settings.

6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas