Causal Inference When Counterfactuals Depend on the Proportion of All Subjects Exposed

Miles, Caleb H.; Petersen, Maya; Laan, Mark J. van der

doi:10.1111/biom.13034

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…Assumption 3 implies that the outcome of one unit depends on the treatment assignment of other units only through the number of those who are assigned to the treatment condition within the same cluster. The assumption has been commonly used in the literature (e.g., Liu & Hudgens, 2014; Miles et al., 2019; Tchetgen Tchetgen & VanderWeele, 2012). It is a reasonable simplification of the interference structure and is directly motivated by two‐stage randomization which varies the proportion of treated units within a cluster.…”

Section: Experimental Design and Causal Quantities Of Interestmentioning

confidence: 99%

Statistical Inference and Power Analysis for Direct and Spillover Effects in Two-Stage Randomized Experiments

2022

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Two‐stage randomized experiments become an increasingly popular experimental design for causal inference when the outcome of one unit may be affected by the treatment assignments of other units in the same cluster. In this paper, we provide a methodological framework for general tools of statistical inference and power analysis for two‐stage randomized experiments. Under the randomization‐based framework, we consider the estimation of a new direct effect of interest as well as the average direct and spillover effects studied in the literature. We provide unbiased estimators of these causal quantities and their conservative variance estimators in a general setting. Using these results, we then develop hypothesis testing procedures and derive sample size formulas. We theoretically compare the two‐stage randomized design with the completely randomized and cluster randomized designs, which represent two limiting designs. Finally, we conduct simulation studies to evaluate the empirical performance of our sample size formulas. For empirical illustration, the proposed methodology is applied to the randomized evaluation of the Indian National Health Insurance Program. An open‐source software package is available for implementing the proposed methodology.

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Section: Experimental Design and Causal Quantities Of Interestmentioning

confidence: 99%

Statistical Inference and Power Analysis for Direct and Spillover Effects in Two-Stage Randomized Experiments

2022

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“…The causal inference literature has developed a number of statistical methods to deal with interference (Aronow and Samii, 2017;Forastiere et al, 2020a;Papadogeorgou et al, 2019;Aronow et al, 2019;Miles et al, 2019;Loh et al, 2020;Tortù et al, 2020;Leung, 2020a). In particular, some recent studies have proposed estimators for treatment and spillover effects under the assumption of on partial (or clustered) interference (Sobel, 2006;Hudgens and Halloran, 2008;Tchetgen and VanderWeele, 2012;Liu and Hudgens, 2014;Liu et al, 2016;Kang and Imbens, 2016;Forastiere et al, 2016;Basse and Feller, 2018;Forastiere et al, 2019a), where units are clustered in exogenous groups and spillover mechanisms are assumed to occur only within groups.…”

Section: Related Literaturementioning

confidence: 99%

Causal Effects with Hidden Treatment Diffusion on Observed or Partially Observed Networks

Tortú,

Crimaldi,

Mealli

et al. 2021

Preprint

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In randomized experiments, interactions between units might generate a treatment diffusion process. This is common when the treatment of interest is an actual object or product that can be shared among peers (e.g., flyers, booklets, videos). For instance, if the intervention of interest is an information campaign realized through the distribution of a video to targeted individuals, some of these treated individuals might share the video they received with their friends. Such a phenomenon is usually unobserved, causing a misallocation of individuals in the two treatment arms: some of the initially untreated units might have actually received the treatment by diffusion. Treatment misclassification can, in turn, introduce a bias in the estimation of the causal effect. Inspired by a recent field experiment on the effect of different types of school incentives aimed at encouraging students to attend cultural events, we present a novel approach to deal with a hidden diffusion process on observed or partially observed networks. Specifically, we develop a simulation-based sensitivity analysis that assesses the robustness of the estimates against the possible presence of a treatment diffusion. We simulate several diffusion scenarios within a plausible range of sensitivity parameters and we compare the treatment effect which is estimated in each scenario with the one that is obtained while ignoring the diffusion process. Results suggest that even a treatment diffusion parameter of small size may lead to a significant bias in the estimation of the treatment effect.

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“…A different strand of the interference literature considers the setting where there is only one group, such as a single connected social network where interference could occur between any of the subjects. See for example [1,26,18,16,24]. The context that we consider here, namely independent groups of size two, falls into the first of these two frameworks.…”

Section: Introductionmentioning

confidence: 99%

Causal Effects in Twin Studies: the Role of Interference

Smith,

Ogburn,

McGue

et al. 2020

Preprint

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The use of twins designs to address causal questions is becoming increasingly popular. A standard assumption is that there is no interference between twins-that is, no twin's exposure has a causal impact on their co-twin's outcome. However, there may be settings in which this assumption would not hold, and this would (1) impact the causal interpretation of parameters obtained by commonly used existing methods; (2) change which effects are of greatest interest; and (3) impact the conditions under which we may estimate these effects. We explore these issues, and we derive semi-parametric efficient estimators for causal effects in the presence of interference between twins. Using data from the Minnesota Twin Family Study, we apply our estimators to assess whether twins' consumption of alcohol in early adolescence may have a causal impact on their co-twins' substance use later in life.

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Causal Inference When Counterfactuals Depend on the Proportion of All Subjects Exposed

Cited by 8 publications

References 20 publications

Statistical Inference and Power Analysis for Direct and Spillover Effects in Two-Stage Randomized Experiments

Statistical Inference and Power Analysis for Direct and Spillover Effects in Two-Stage Randomized Experiments

Causal Effects with Hidden Treatment Diffusion on Observed or Partially Observed Networks

Causal Effects in Twin Studies: the Role of Interference

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