Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap
This work addresses a key challenge in causal inference for researchers and practitioners, but it is incremental as it builds on existing methods for handling overlap issues.
The paper tackles the problem of poor overlap in causal effect estimation by introducing deconfounding scores, which are feature representations that improve overlap without introducing confounding bias, and demonstrates in simulations that these scores can lead to estimators with good finite-sample properties as an alternative to standard regularizations.
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.