# Permutation Weighting

@inproceedings{Arbour2021PermutationW, title={Permutation Weighting}, author={David T. Arbour and Drew Dimmery}, booktitle={ICML}, year={2021} }

This work introduces permutation weighting: a weighting estimator for observational causal inference under general treatment regimes which preserves arbitrary measures of covariate balance. We show that estimating weights which obey balance constraints is equivalent to a simple binary classification problem between the observed data and a permuted dataset (no matter the cardinality of treatment). Arbitrary probabilistic classifiers may be used in this method; the hypothesis space of the…

## 3 Citations

A Balancing Weight Framework for Estimating the Causal Effect of General Treatments

- Mathematics
- 2020

In observational studies, weighting methods that directly optimize the balance between treatment and covariates have received much attention lately; however these have mainly focused on binary…

Kernel Optimal Orthogonality Weighting: A Balancing Approach to Estimating Effects of Continuous Treatments

- Mathematics
- 2019

Many scientific questions require estimating the effects of continuous treatments. Outcome modeling and weighted regression based on the generalized propensity score are the most commonly used…

Counterfactual Prediction for Bundle Treatment

- Computer Science, EconomicsNeurIPS
- 2020

This work proposes a novel variational sample re-weighting (VSR) method to eliminate confounding bias by decorrelating the treatments and confounders and conducts extensive experiments to demonstrate that the predictive model trained on this re-weightsed dataset can achieve more accurate counterfactual outcome prediction.

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