Large sample randomization inference of causal effects in the presence of interference
By Lan Liu, Michael G. Hudgens. 2014
143 citations recorded by the source corpus.
Recently, there has been increasing interest in making causal inference when interference is possible. In the presence of interference, treatment may have several types of effects. In this article, we consider inference about such effects when the population consists of groups of individuals where interference is possible within groups but not between groups. A two-stage randomization design is assumed where in the first stage groups are randomized to different treatment allocation strategies and in the second stage individuals are randomized to treatment or control conditional on the strategy assigned to their group in the first stage. For this design, the asymptotic distributions of estimators of the causal effects are derived when either the number of individuals per group or the number of groups grows large. Under certain homogeneity assumptions, the asymptotic distributions provide justification for Wald-type confidence intervals (CIs) and tests. Empirical results demonstrate that the Wald CIs have good coverage in finite samples and are narrower than CIs based on either the Chebyshev or Hoeffding inequalities provided the number of groups is not too small. The methods are illustrated by two examples which consider the effects of cholera vaccination and an intervention to encourage voting.
Subfields: Statistics and Probability
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
- Cynthia Rudin
- Peng Ding
- Alexander Volfovsky
- Sudeepa Roy
- Xinran Li
- Susan Athey
- Guido W. Imbens
- Stefan Wager
Citations in the corpus
- The Econometrics of Randomized Experiments (2016, background): “Like Liu and Hudgens (2013) and Athey, Eckles and Imbens (2015), they consider randomization inference.”
- Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference (2020, background): “First, there has been a growing body of work on the design of novel randomization schemes to perform causal inference under interference (Liu and Hudgens, 2014; Sinclair et al., 2012; Duflo and Saez, 2003; Basse and Airoldi, 2018).”
- General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference (2016, methodology): “For more applications in nonparametric tests and randomization-based causal inference, please see Lehmann (1975), Liu and Hudgens (2014), Ding (2017), and Ding and Dasgupta (2016).”
- Identification and Estimation of Spillover Effects in Randomized Experiments (2017, methodology): “…setting, identification issues are largely absent from this literature, and focus is placed mainly on p-values, variance and confidence interval calculations (Rosenbaum, 2007; Hudgens and Halloran, 2008; Tchetgen Tchetgen and VanderWeele, 2012; Liu and Hudgens, 2014; Basse and Feller, 2018).”