Journal article

Estimating causal effects: considering three alternatives to difference-in-differences estimation

Stephen O'Neill, Noemi Kreif, Richard Grieve, Matthew Sutton, Jasjeet S Sekhon



Difference-in-differences (DiD) estimators provide unbiased treatment effect estimates when, in the absence of treatment, the average outcomes for the treated and control groups would have followed parallel trends over time. This assumption is implausible in many settings. An alternative assumption is that the potential outcomes are independent of treatment status, conditional on past outcomes. This paper considers three methods that share this assumption: the synthetic control method, a lagged dependent variable (LDV) regression approach, and matching on past outcomes. Our motivating empirical study is an evaluation of a hospital pay-for-performance scheme in England, the best practice tari..

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Awarded by National Institute for Health Research

Awarded by Medical Research Council

Awarded by Economic and Social Research Council

Funding Acknowledgements

This report is independent research supported by the National Institute for Health Research (Senior Research Fellowship, Dr Richard Grieve, SRF-2013-06-016) and the Medical Research Council (Early Career Fellowship in the Economics of Health, Dr Noemi Kreif MR/L012332/1). This research is partly funded by research commissioned and funded by the Policy Research Programme in the Department of Health to the Policy Innovation Research Unit (PIRU) at LSHTM. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.