Generalized coarsened confounding for causal effects: a large-sample framework
Debashis Ghosh, Lei Wang
TL;DR
This paper introduces a new framework for handling confounding in causal inference using generalized coarsened procedures and provides theoretical guarantees for the methods.
Contribution
The paper introduces two new algorithms and a general asymptotic framework for generalized coarsened confounding.
Findings
The proposed framework provides asymptotic results for the average causal effect estimator.
Conditions for consistency and an asymptotic justification for variance formulae are established.
A bias correction technique is introduced and applied to real observational data.
Abstract
There has been widespread use of causal inference methods for the rigorous analysis of observational studies and to identify policy evaluations. In this article, we consider a class of generalized coarsened procedures for confounding. At a high level, these procedures can be viewed as performing a clustering of confounding variables, followed by treatment effect and attendant variance estimation using the confounder strata. In addition, we propose two new algorithms for generalized coarsened confounding. While previous authors have developed some statistical properties for one special case in our class of procedures, we instead develop a general asymptotic framework. We provide asymptotic results for the average causal effect estimator as well as providing conditions for consistency. In addition, we provide an asymptotic justification for the variance formulae for coarsened exact…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Inference · Statistical Methods and Bayesian Inference