Longitudinal weighted and trimmed treatment effects with flip interventions

TL;DR

This paper introduces a novel approach using flip interventions to extend weighting and trimming methods for causal inference in longitudinal data, effectively addressing positivity violations and enabling policy-relevant effect estimation.

Contribution

It develops a new class of flip interventions for longitudinal data, providing identifiable, interpretable, and implementable weighted treatment effects under positivity violations.

Findings

Flip interventions yield a broad class of weighted average treatment effects.

The proposed estimators are multiply robust, efficient, and achieve root-n consistency.

Application demonstrates the method's utility in analyzing union membership effects on earnings.

Abstract

Weighting and trimming are popular methods for addressing positivity violations in causal inference. While well-studied with single-timepoint data, standard methods do not easily generalize to address non-baseline positivity violations in longitudinal data, and remain vulnerable to such violations. In this paper, we extend weighting and trimming to longitudinal data via stochastic ``flip'' interventions, which maintain the treatment status of subjects who would have received the target treatment, and flip others' treatment to the target with probability equal to their weight (e.g., overlap weight, trimming indicator). We first show, in single-timepoint data, that flip interventions yield a large class of weighted average treatment effects, ascribing a novel policy interpretation to these popular weighted estimands. With longitudinal data, we then show that flip interventions provide interpretable weighting or trimming on non-baseline covariates and, crucially, yield effects that are identifiable under arbitrary positivity violations. Moreover, we demonstrate that flip interventions are policy-relevant since they could be implemented in practice. By contrast, we show that alternative approaches for weighting on non-baseline covariates fail to achieve this property. We derive flexible and efficient estimators based on efficient influence functions when the weight is a smooth function of the propensity score. Namely, we construct multiply robust-style and sequentially doubly robust-style estimators that achieve root-n consistency and asymptotic normality under nonparametric conditions. Finally, we demonstrate our methods through an analysis of the effect of union membership on earnings.