• Corpus ID: 88524272

Permutation Weighting

  title={Permutation Weighting},
  author={David T. Arbour and Drew Dimmery},
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… 
A Balancing Weight Framework for Estimating the Causal Effect of General Treatments
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
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
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.


Kernel Balancing: A Flexible Non-Parametric Weighting Procedure for Estimating Causal Effects
Methods such as matching and weighting for causal effect estimation attempt to adjust the joint distribution of observed covariates for treated and control units to be the same. However, they often
Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies
Entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments, obviates the need for continual balance checking and iterative searching over propensity score models that may stochastically balance the covariate moments.
Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data
Weighting methods that adjust for observed covariates, such as inverse probability weighting, are widely used for causal inference and estimation with incomplete outcome data. Part of the appeal of
Kernel-based covariate functional balancing for observational studies.
This work proposes a method that attains uniform approximate balance for covariate functions in a reproducing-kernel Hilbert space and shows that it achieves better balance with smaller sampling variability than existing methods.
Covariate balancing propensity score by tailored loss functions
In observational studies, propensity scores are commonly estimated by maxi- mum likelihood but may fail to balance high-dimensional pre-treatment covariates even after specification search. We
Constructing inverse probability weights for marginal structural models.
The authors describe possible tradeoffs that an epidemiologist may encounter when attempting to make inferences and weight truncation is presented as an informal and easily implemented method to deal with these tradeoffs.
Causal Inference With General Treatment Regimes
In this article we develop the theoretical properties of the propensity function, which is a generalization of the propensity score of Rosenbaum and Rubin. Methods based on the propensity score have
Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements
The covariate balancing generalized propensity score (CBGPS) methodology is proposed, which minimizes the association between covariates and the treatment, and both parametric and nonparametric approaches show their superior performance over the standard maximum likelihood estimation in a simulation study.
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is unconfounded, that is, independent of the potential outcomes given
Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise
This paper proposes a novel causal inference algorithm called least-squares independence regression (LSIR), which learns the additive noise model through minimization of an estimator of the squared-loss mutual information between inputs and residuals.