Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dimension-Free Average Treatment Effect Inference with Deep Neural
  Networks   This paper investigates the estimation and inference of the average treatment
effect (ATE) using deep neural networks (DNNs) in the potential outcomes
framework. Under some regularity conditions, the observed response can be
formulated as the response of a mean regression problem with both the
confounding variables and the treatment indicator as the independent variables.
Using such formulation, we investigate two methods for ATE estimation and
inference based on the estimated mean regression function via DNN regression
using a specific network architecture. We show that both DNN estimates of ATE
are consistent with dimension-free consistency rates under some assumptions on
the underlying true mean regression model. Our model assumptions accommodate
the potentially complicated dependence structure of the observed response on
the covariates, including latent factors and nonlinear interactions between the
treatment indicator and confounding variables. We also establish the asymptotic
normality of our estimators based on the idea of sample splitting, ensuring
precise inference and uncertainty quantification. Simulation studies and real
data application justify our theoretical findings and support our DNN
estimation and inference methods.

24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph