Discussion of "On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning"

TL;DR

This paper introduces a new model-checking method for causal inference that assesses whether the bias in doubly robust estimators is small enough to ensure reliable confidence intervals, enhancing the robustness of causal estimates.

Contribution

It presents a novel approach for evaluating bias in causal estimates, providing a model-checking tool that complements existing confidence interval methods.

Findings

Enables assessment of bias relative to standard error in causal estimates

Provides a model-checking procedure for doubly robust estimators

Improves reliability of confidence intervals in causal inference

Abstract

We congratulate the authors on their exciting paper, which introduces a novel idea for assessing the estimation bias in causal estimates. Doubly robust estimators are now part of the standard set of tools in causal inference, but a typical analysis stops with an estimate and a confidence interval. The authors give an approach for a unique type of model-checking that allows the user to check whether the bias is sufficiently small with respect to the standard error, which is generally required for confidence intervals to be reliable.