@inproceedings{
clivio2023towards,
title={Towards representation learning for general weighting problems in causal inference},
author={Oscar Clivio and Avi Feller and Christopher C. Holmes},
booktitle={Causal Representation Learning Workshop at NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=gNMANHvjO1}
}
Weighting problems in treatment effect estimation can be solved by minimising an appropriate probability distance. Choosing which distance to minimise, however, can be challenging as it depends on the unknown data generating process. An alternative is to instead choose a distance that depends on a suitable representation of covariates. In this work, we give errors that quantify the bias added to a weighting estimator when using a representation, giving clear objectives to minimise when learning the representation and generalising a large body of previous work on deconfounding, prognostic, balancing and propensity scores. We further outline a method minimising such objectives, and show promising numerical results on two semi-synthetic datasets.
Towards representation learning for general weighting problems in causal inference
Oscar Clivio Avi Feller Christopher C. Holmes