Richard Nielsen

Publications (3)54.32 Total impact

• Article: Medicare Health Support Pilot Program.

  Gary King, Richard Nielsen, Aaron Wells
  New England Journal of Medicine 02/2012; 366(7):667; author reply 667-8. · 53.30 Impact Factor
• Source

  Available from: harvard.edu

  Article: Comparative Effectiveness of Matching Methods for Causal Inference

  Gary King, Richard Nielsen, Carter Coberley, James E Pope, Aaron Wells, Alberto Abadie, Seth Hill, Kosuke Imai, John Londregan, Adam Meirowitz, Brandon Stewart, Liz Stuart
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  ABSTRACT: Matching methods for causal inference selectively prune observations from the data in order to reduce model dependence. They are successful when simultaneously maximizing balance (between the treated and control groups on the pre-treatment covariates) and the number of observations remaining in the data set. However, ex-isting matching methods either fix the matched sample size ex ante and attempt to reduce imbalance as a result of the procedure (e.g., propensity score and Mahalanobis distance matching) or fix imbalance ex ante and attempt to lose as few observations as possible ex post (e.g., coarsened exact matching and calpier-based approaches). As an alternative, we offer a simple graphical approach that addresses both criteria simultaneously and lets the user choose a matching solution from the imbalance-sample size frontier. In the process of applying our approach, we also discover that propensity score matching (PSM) often approximates random matching, both in real applications and in data simulated by the processes that fit PSM theory. Moreover, contrary to conventional wisdom, random matching is not benign: it (and thus often PSM) can degrade inferences relative to not matching at all. Other methods we study do not have these or other problems we describe. However, with our easy-to-use graphical approach, users can focus on choosing a matching solution for a particular application rather than whatever method happened to be used to generate it.
  01/2012;
• Source

  Available from: harvard.edu

  Article: Avoiding randomization failure in program evaluation, with application to the Medicare Health Support program.

  Gary King, Richard Nielsen, Carter Coberley, James E Pope, Aaron Wells
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  ABSTRACT: We highlight common problems in the application of random treatment assignment in large-scale program evaluation. Random assignment is the defining feature of modern experimental design, yet errors in design, implementation, and analysis often result in real-world applications not benefiting from its advantages. The errors discussed here cover the control of variability, levels of randomization, size of treatment arms, and power to detect causal effects, as well as the many problems that commonly lead to post-treatment bias. We illustrate these issues by identifying numerous serious errors in the Medicare Health Support evaluation and offering recommendations to improve the design and analysis of this and other large-scale randomized experiments.
  Population Health Management 02/2011; 14 Suppl 1:S11-22. · 1.02 Impact Factor