Causal Inference Under Interference And Network Uncertainty
Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser
Abstract
Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly, but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.
BibTeX
@InProceedings{pmlr-v115-bhattacharya20a,
title = {Causal Inference Under Interference And Network Uncertainty},
author = {Bhattacharya, Rohit and Malinsky, Daniel and Shpitser, Ilya},
booktitle = {Proceedings of The 35th Uncertainty in Artificial Intelligence Conference},
pages = {1028--1038},
year = {2020},
editor = {Adams, Ryan P. and Gogate, Vibhav},
volume = {115},
series = {Proceedings of Machine Learning Research},
month = {22--25 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v115/bhattacharya20a/bhattacharya20a.pdf},
url = {https://proceedings.mlr.press/v115/bhattacharya20a.html},
abstract = {Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly, but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.}
}