Journal of Causal Inference最新文献

Orthogonal meta-learners, such as DR-learner (Kennedy EH. Towards optimal doubly robust estimation of heterogeneous causal effects. arXiv preprint arXiv:2004.14497 2020), R-learner (Nie X, Wager S. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika 2021;108:299-319) and IF-learner (Curth A, Alaa AM, van der Schaar M. Estimating structural target functions using machine learning and influence functions. arXiv preprint arXiv:2008.06461 2020), are increasingly used to estimate conditional average treatment effects. They are hoped to improve convergence rates relative to naïve meta-learners (e.g., T-, S- and X-learner (Künzel SR, Sekhon JS, Bickel PJ, Yu B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proc Natl Acad Sci 2019;116:4156-65)) through de-biasing procedures that involve applying standard learners to specifically transformed outcome data. This leads them to disregard the possibly constrained outcome space, which can be particularly problematic for dichotomous outcomes: these typically get transformed to values that are no longer constrained to the unit interval, which may cause instability and makes it difficult for standard learners to guarantee predictions within the unit interval. To address this, we construct a non-orthogonal imputation-learner and an orthogonal 'i-learner' for the prediction of counterfactual outcomes, which respect the outcome space. These are more generally expected to outperform existing learners, even when the outcome is unconstrained, as we confirm empirically in simulation studies and an analysis of critical care data. Our development also sheds broader light onto the construction of orthogonal learners for other estimands.

There has been widespread use of causal inference methods for the rigorous analysis of observational studies and to identify policy evaluations. In this article, we consider a class of generalized coarsened procedures for confounding. At a high level, these procedures can be viewed as performing a clustering of confounding variables, followed by treatment effect and attendant variance estimation using the confounder strata. In addition, we propose two new algorithms for generalized coarsened confounding. While previous authors have developed some statistical properties for one special case in our class of procedures, we instead develop a general asymptotic framework. We provide asymptotic results for the average causal effect estimator as well as providing conditions for consistency. In addition, we provide an asymptotic justification for the variance formulae for coarsened exact matching. A bias correction technique is proposed, and we apply the proposed methodology to data from two well-known observational studies.

In an observational study, it is common to leverage known null effects to detect bias. One such strategy is to set aside a placebo sample - a subset of data immune from the hypothesized cause-and-effect relationship. Existence of an effect in the placebo sample raises concerns about unmeasured confounding bias while absence of it helps corroborate the causal conclusion. This paper describes a framework for using a placebo sample to detect and remove bias. We state the identification assumptions and develop estimation and inference methods based on outcome regression, inverse probability weighting, and doubly-robust approaches. Simulation studies investigate the finite-sample performance of the proposed methods. We illustrate the methods using an empirical study of the effect of the earned income tax credit on infant health.

Many software packages have been developed to assist researchers in drawing directed acyclic graphs (DAGs), each with unique functionality and usability. We examine five of the most common software to generate DAGs: TikZ, DAGitty, ggdag, dagR, and igraph. For each package, we provide a general description of its background, analysis and visualization capabilities, and user-friendliness. Additionally in order to compare packages, we produce two DAGs in each software, the first featuring a simple confounding structure, while the second includes a more complex structure with three confounders and a mediator. We provide recommendations for when to use each software depending on the user's needs.