A framework for causal inference in the presence of extreme inverse probability weights: the role of overlap weights

by   Roland A. Matsouaka, et al.

In this paper, we consider recent progress in estimating the average treatment effect when extreme inverse probability weights are present and focus on methods that account for a possible violation of the positivity assumption. These methods aim at estimating the treatment effect on the subpopulation of patients for whom there is a clinical equipoise. We propose a systematic approach to determine their related causal estimands and develop new insights into the properties of the weights targeting such a subpopulation. Then, we examine the roles of overlap weights, matching weights, Shannon's entropy weights, and beta weights. This helps us characterize and compare their underlying estimators, analytically and via simulations, in terms of the accuracy, precision, and root mean squared error. Moreover, we study the asymptotic behaviors of their augmented estimators (that mimic doubly robust estimators), which lead to improved estimations when either the propensity or the regression models are correctly specified. Based on the analytical and simulation results, we conclude that overall overlap weights are preferable to matching weights, especially when there is moderate or extreme violations of the positivity assumption. Finally, we illustrate the methods using a real data example marked by extreme inverse probability weights.



There are no comments yet.


page 1

page 2

page 3

page 4


Propensity score weighting under limited overlap and model misspecification

Propensity score (PS) weighting methods are often used in non-randomized...

Propensity Score Weighting for Causal Inference with Multi-valued Treatments

This article proposes a unified framework, the balancing weights, for es...

On the implied weights of linear regression for causal inference

In this paper, we derive and analyze the implied weights of linear regre...

Estimating Heterogeneous Survival Treatment Effect via Machine/Deep Learning Methods in Observational Studies

The rise of personalized medicine necessitates improved causal inference...

Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching

In causal inference, a variety of causal effect estimands have been stud...

Outlier-Resistant Estimators for Average Treatment Effect in Causal Inference

Estimators for causal quantities sometimes suffer from outliers. We inve...

Entropy Balancing for Continuous Treatments

This paper introduces entropy balancing for continuous treatments (EBCT)...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.