• Publications
  • Influence
Matching methods for causal inference: A review and a look forward.
  • E. Stuart
  • Economics
    Statistical science : a review journal of the…
  • 1 February 2010
TLDR
A structure for thinking about matching methods and guidance on their use is provided, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
TLDR
A unified approach is proposed that makes it possible for researchers to preprocess data with matching and then to apply the best parametric techniques they would have used anyway and this procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
TLDR
MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions.
Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies
TLDR
A suite of quantitative and qualitative methods are described that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample to contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data.
Multiple imputation by chained equations: what is it and how does it work?
TLDR
This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method.
Improving propensity score weighting using machine learning
TLDR
The authors examine the performance of various CART‐based propensity score models using simulated data and suggest that ensemble methods, especially boosted CART, may be useful for propensity score weighting.
The use of propensity scores to assess the generalizability of results from randomized trials
TLDR
These metrics can serve as a first step in assessing the generalizability of results from randomized trials to target populations, and are illustrated using data on the evaluation of a schoolwide prevention program called Positive Behavioral Interventions and Supports.
Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research.
TLDR
The authors give an overview of available techniques for PS estimation and PS application and provide a way to help compare PS techniques, using the resulting measured covariate balance as the criterion for selecting between techniques.
Misunderstandings between experimentalists and observationalists about causal inference
Summary.  We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference. These issues concern some of the most fundamental advantages and
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