Propensity Score Matching for Causal Inference with Relational Data
Abstract
Propensity score matching (PSM) is a widely used method for performing causal inference with observational data. PSM requires fully specifying the set of confounding variables of treatment and outcome. In the case of relational data, this set may include non-intuitive relational variables, i.e., variables derived from the relational structure of the data. In this work, we provide an automated method to derive these relational variables based on the relational structure and a set of naive confounders. This automatic construc-tion includes two unusual classes of variables: relational degree and entity identifiers. We provide experimental evidence that demon-strates the utility of these variables in ac-counting for certain latent confounders. Fi-nally, through a set of synthetic experiments, we show that our method improves the per-formance of PSM for causal inference with relational data. 1
Cite
Text
Arbour et al. "Propensity Score Matching for Causal Inference with Relational Data." Conference on Uncertainty in Artificial Intelligence, 2014.Markdown
[Arbour et al. "Propensity Score Matching for Causal Inference with Relational Data." Conference on Uncertainty in Artificial Intelligence, 2014.](https://mlanthology.org/uai/2014/arbour2014uai-propensity/)BibTeX
@inproceedings{arbour2014uai-propensity,
title = {{Propensity Score Matching for Causal Inference with Relational Data}},
author = {Arbour, David T. and Marazopoulou, Katerina and Garant, Dan and Jensen, David D.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2014},
pages = {25-34},
url = {https://mlanthology.org/uai/2014/arbour2014uai-propensity/}
}