Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap

by   Alexander D'Amour, et al.

A key condition for obtaining reliable estimates of the causal effect of a treatment is overlap (a.k.a. positivity): the distributions of the features used to perform causal adjustment cannot be too different in the treated and control groups. In cases where overlap is poor, causal effect estimators can become brittle, especially when they incorporate weighting. To address this problem, a number of proposals (including confounder selection or dimension reduction methods) incorporate feature representations to induce better overlap between the treated and control groups. A key concern in these proposals is that the representation may introduce confounding bias into the effect estimator. In this paper, we introduce deconfounding scores, which are feature representations that induce better overlap without biasing the target of estimation. We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data. As a proof of concept, we characterize a family of deconfounding scores in a simplified setting with Gaussian covariates, and show that in some simple simulations, these scores can be used to construct estimators with good finite-sample properties. In particular, we show that this technique could be an attractive alternative to standard regularizations that are often applied to IPW and balancing weights.


page 1

page 2

page 3

page 4


Addressing Extreme Propensity Scores in Estimating Counterfactual Survival Functions via the Overlap Weights

The inverse probability weighting approach is popular for evaluating tre...

Large Sample Properties of Entropy Balancing Estimators of Average Causal Effects

Weighting methods are used in observational studies to adjust for covari...

Propensity score weighting under limited overlap and model misspecification

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

Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data

The foremost challenge to causal inference with real-world data is to ha...

Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects

In the absence of unobserved confounders, matching and weighting methods...

Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect

In this paper we study the finite sample and asymptotic properties of va...

Confounding Adjustment Methods for Multi-level Treatment Comparisons Under Lack of Positivity and Unknown Model Specification

Imbalances in covariates between treatment groups are frequent in observ...