Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Estimation of Causal Effects from Observational Data   Many modern applications collect data that comes in federated spirit, with
data kept locally and undisclosed. Till date, most insight into the causal
inference requires data to be stored in a central repository. We present a
novel framework for causal inference with federated data sources. We assess and
integrate local causal effects from different private data sources without
centralizing them. Then, the treatment effects on subjects from observational
data using a non-parametric reformulation of the classical potential outcomes
framework is estimated. We model the potential outcomes as a random function
distributed by Gaussian processes, whose defining parameters can be efficiently
learned from multiple data sources, respecting privacy constraints. We
demonstrate the promise and efficiency of the proposed approach through a set
of simulated and real-world benchmark examples.

11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
2
The categories of this paper are: math