Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Debiased Machine Learning for Dynamic Treatment Effects   We extend the idea of automated debiased machine learning to the dynamic
treatment regime. We show that the multiply robust formula for the dynamic
treatment regime with discrete treatments can be re-stated in terms of a
recursive Riesz representer characterization of nested mean regressions. We
then apply a recursive Riesz representer estimation learning algorithm that
estimates de-biasing corrections without the need to characterize how the
correction terms look like, such as for instance, products of inverse
probability weighting terms, as is done in prior work on doubly robust
estimation in the dynamic regime. Our approach defines a sequence of loss
minimization problems, whose minimizers are the mulitpliers of the de-biasing
correction, hence circumventing the need for solving auxiliary propensity
models and directly optimizing for the mean squared error of the target
de-biasing correction.

20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat