Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Individual Treatment Prescription Effect Estimation in a Low Compliance
  Setting   Individual Treatment Effect (ITE) estimation is an extensively researched
problem, with applications in various domains. We model the case where there
exists heterogeneous non-compliance to a randomly assigned treatment, a typical
situation in health (because of non-compliance to prescription) or digital
advertising (because of competition and ad blockers for instance). The lower
the compliance, the more the effect of treatment prescription, or individual
prescription effect (IPE), signal fades away and becomes hard to estimate. We
propose a new approach for the estimation of the IPE that takes advantage of
observed compliance information to prevent signal fading. Using the Structural
Causal Model framework and do-calculus, we define a general mediated causal
effect setting and propose a corresponding estimator which consistently
recovers the IPE with asymptotic variance guarantees. Finally, we conduct
experiments on both synthetic and real-world datasets that highlight the
benefit of the approach, which consistently improves state-of-the-art in low
compliance settings

29
The categories of this paper are: funct-an
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph