• Publications
  • Influence
Matching methods for causal inference: A review and a look forward.
  • E. Stuart
  • Computer Science, Medicine
  • Statistical science : a review journal of the…
  • 1 February 2010
This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature is now and where it should be headed. Expand
  • 2,645
  • 274
  • PDF
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
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 by preprocessing data with nonparametric and semi-parametric matching methods. Expand
  • 1,730
  • 103
  • PDF
Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies
The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment receivedExpand
  • 1,095
  • 72
Improving propensity score weighting using machine learning.
Machine learning techniques such as classification and regression trees (CART) have been suggested as promising alternatives to logistic regression for estimation of propensity scores. Expand
  • 515
  • 31
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 andExpand
  • 648
  • 27
  • PDF
Using full matching to estimate causal effects in nonexperimental studies: examining the relationship between adolescent marijuana use and adult outcomes.
Matching methods such as nearest neighbor propensity score matching are increasingly popular techniques for controlling confounding in nonexperimental studies. However, simple k:1 matching methods,Expand
  • 203
  • 27
The use of propensity scores to assess the generalizability of results from randomized trials.
Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: Will the program be effective in aExpand
  • 305
  • 25
  • PDF
Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research.
There is considerable interest in using propensity score (PS) statistical techniques to address questions of causal inference in psychological research. Many PS techniques exist, yet few guidelinesExpand
  • 475
  • 24
Community Violence and Youth: Affect, Behavior, Substance Use, and Academics
Community violence is recognized as a major public health problem (WHO, World Report on Violence and Health,2002) that Americans increasingly understand has adverse implications beyond inner-cities.Expand
  • 181
  • 21