tmleCommunity: A R Package Implementing Target Maximum Likelihood Estimation for Community-level Data

TL;DR

The paper introduces the tmleCommunity R package, which implements targeted maximum likelihood estimation for assessing community-level interventions on individual outcomes, supporting flexible interventions and machine learning integration.

Contribution

It provides a new software tool for causal inference in community-level studies, enabling stochastic intervention analysis with advanced machine learning methods.

Findings

Supports multivariate static, dynamic, and stochastic interventions.

Allows integration of user-specified machine learning algorithms.

Facilitates causal effect estimation at a community level.

Abstract

Over the past years, many applications aim to assess the causal effect of treatments assigned at the community level, while data are still collected at the individual level among individuals of the community. In many cases, one wants to evaluate the effect of a stochastic intervention on the community, where all communities in the target population receive probabilistically assigned treatments based on a known specified mechanism (e.g., implementing a community-level intervention policy that target stochastic changes in the behavior of a target population of communities). The tmleCommunity package is recently developed to implement targeted minimum loss-based estimation (TMLE) of the effect of community-level intervention(s) at a single time point on an individual-based outcome of interest, including the average causal effect. Implementations of the inverse-probability-of-treatment-weighting (IPTW) and the G-computation formula (GCOMP) are also available. The package supports multivariate arbitrary (i.e., static, dynamic or stochastic) interventions with a binary or continuous outcome. Besides, it allows user-specified data-adaptive machine learning algorithms through SuperLearner, sl3 and h2oEnsemble packages. The usage of the tmleCommunity package, along with a few examples, will be described in this paper.