Randomization tests of causal effects under interference

By Guillaume W. Basse, Avi Feller, Panos Toulis. Biometrika, 2019

100 citations recorded by the source corpus.

Many causal questions involve interactions between units, also known as interference, for example between individuals in households, students in schools, or firms in markets. In this paper we formalize the concept of a conditioning mechanism, which provides a framework for constructing valid and powerful randomization tests under general forms of interference. We describe our framework in the context of twostage randomized designs and apply our approach to a randomized evaluation of an intervention targeting student absenteeism in the school district of Philadelphia. We show improvements over existing methods in terms of computational and statistical power.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8fe39f0fe37ffcd4e8da077c84914ed0cfdba367

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Jason A. Wu
2. Peng Ding
3. Xinran Li
4. Kevin Guo
5. Guillaume W. Basse

Citations in the corpus

• Randomization Tests for Weak Null Hypotheses in Randomized Experiments (2018, background): “It relies on partitioning the space of all randomizations, and in some subspaces, certain test statistics have known distributions under the weak null hypotheses (Athey et al. 2018; Basse et al. 2019).”