Model-Agnostic Covariate-Assisted Inference on Partially Identified
Causal Effects
- URL: http://arxiv.org/abs/2310.08115v1
- Date: Thu, 12 Oct 2023 08:17:30 GMT
- Title: Model-Agnostic Covariate-Assisted Inference on Partially Identified
Causal Effects
- Authors: Wenlong Ji, Lihua Lei, Asher Spector
- Abstract summary: Many causal estimands are only partially identifiable since they depend on the unobservable joint distribution between potential outcomes.
We propose a unified and model-agnostic inferential approach for a wide class of partially identified estimands.
- Score: 2.1638817206926855
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many causal estimands are only partially identifiable since they depend on
the unobservable joint distribution between potential outcomes. Stratification
on pretreatment covariates can yield sharper partial identification bounds;
however, unless the covariates are discrete with relatively small support, this
approach typically requires consistent estimation of the conditional
distributions of the potential outcomes given the covariates. Thus, existing
approaches may fail under model misspecification or if consistency assumptions
are violated. In this study, we propose a unified and model-agnostic
inferential approach for a wide class of partially identified estimands, based
on duality theory for optimal transport problems. In randomized experiments,
our approach can wrap around any estimates of the conditional distributions and
provide uniformly valid inference, even if the initial estimates are
arbitrarily inaccurate. Also, our approach is doubly robust in observational
studies. Notably, this property allows analysts to use the multiplier bootstrap
to select covariates and models without sacrificing validity even if the true
model is not included. Furthermore, if the conditional distributions are
estimated at semiparametric rates, our approach matches the performance of an
oracle with perfect knowledge of the outcome model. Finally, we propose an
efficient computational framework, enabling implementation on many practical
problems in causal inference.
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