Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Differentially Private Estimation of Heterogeneous Causal Effects   Estimating heterogeneous treatment effects in domains such as healthcare or
social science often involves sensitive data where protecting privacy is
important. We introduce a general meta-algorithm for estimating conditional
average treatment effects (CATE) with differential privacy (DP) guarantees. Our
meta-algorithm can work with simple, single-stage CATE estimators such as
S-learner and more complex multi-stage estimators such as DR and R-learner. We
perform a tight privacy analysis by taking advantage of sample splitting in our
meta-algorithm and the parallel composition property of differential privacy.
In this paper, we implement our approach using DP-EBMs as the base learner.
DP-EBMs are interpretable, high-accuracy models with privacy guarantees, which
allow us to directly observe the impact of DP noise on the learned causal
model. Our experiments show that multi-stage CATE estimators incur larger
accuracy loss than single-stage CATE or ATE estimators and that most of the
accuracy loss from differential privacy is due to an increase in variance, not
biased estimates of treatment effects.

37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph