☆ 4.5 Editorial Material

Discussion on: Instrumented difference-in-differences, by Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy and Dylan S. Small

BIOMETRICS (2023)

Instrumented difference-in-differences

Ting Ye et al.

Summary: This article introduces a causal inference method based on observational studies, which estimates treatment effects by leveraging exogenous randomness in the exposure trend. A new method called instrumented difference-in-differences is proposed, along with corresponding estimators, and their properties are analyzed. The method is also extended to a two-sample design.

BIOMETRICS (2023)

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Article Statistics & Probability

Demystifying Statistical Learning Based on Efficient Influence Functions

Oliver Hines et al.

Summary: This article discusses the issues with parametric modeling and data-adaptive modeling in evaluating treatment effects and estimands, and introduces the use of efficient influence function to construct estimators in statistical/machine learning. The article also highlights the conditions for these estimators to perform well and demonstrates their broad applicability through diverse examples.

AMERICAN STATISTICIAN (2022)

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Article Statistics & Probability