Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approaches to spatial confounding in geostatistics   Research in the past few decades has discussed the concept of "spatial
confounding" but has provided conflicting definitions and proposed solutions,
some of which do not address the issue of confounding as it is understood in
the field of causal inference. We give a clear account of spatial confounding
as the existence of an unmeasured confounding variable with a spatial
structure. Under certain conditions, including the smoothness of the confounder
as a function of space, we show that spatial covariates (e.g., latitude and
longitude) can be handled as typical covariates by algorithms popular in causal
inference. We focus on "double machine learning" (DML) by which flexible models
are fit for both the exposure and outcome variables to arrive at a causal
estimator with favorable convergence properties. These models avoid restrictive
assumptions, such as linearity and heterogeneity, which are present in linear
models typically employed in spatial statistics and which can lead to strong
bias when violated. We demonstrate the advantages of the DML approach
analytically and via extensive simulation studies.

13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math